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TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

24.10.2024

LSD SLAM received the ECCV 2024 Koenderink Award for standing the Test of Time.

03.07.2024

We have seven papers accepted to ECCV 2024. Check our publication page for more details.

09.06.2024
GCPR / VMV 2024

GCPR / VMV 2024

We are organizing GCPR / VMV 2024 this fall.

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

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Lecture: Visual Navigation for Flying Robots

In recent years, flying robots such as quadcopters have gained increased interest in robotics and computer vision research. For navigating safely, these robots need the ability to localize themselves autonomously using their onboard sensors. Potential applications of such systems include the autonomous 3D reconstruction of buildings, inspection and simple maintenance tasks, surveillance of public places as well as in search and rescue systems.

(6 ECTS, changed on 12.7.2012)

December 07, 2012: This course has been distinguished with the Teach Inf Award 2011/12 at our faculty for the best lecture in computer science in the summer term 2012. Thank you very much for your support!

Content

In this course, we will provide an overview of current techniques for 3D localization, mapping and navigation that are suitable for quadcopters. This course will cover the following topics:

- necessary background on robot hardware, sensors, 3D transformations

- motion estimation from images (including interest point detection, feature descriptors, robust estimation, visual odometry, iteratively closest point)

- filtering techniques and data fusion

- non-linear minimization, bundle adjustment, place recognition, 3D reconstruction

- autonomous navigation and exploration of unknown environments

The lecture will be accompanied by a lab course where the students will implement their own visual navigation system. This course is an excellent preparation for a master thesis project in this area.

Lab Course

Exercise sheets will be passed out every other week, containing both theoretical problems and programming exercises (in C++). In an exercise group every other week, we will discuss the solutions to the theory problems and the programming problems. Active participation in the exercises is the requirement for participation in the final exam. This will be written or oral, depending on the number of attendees. The questions will cover all material presented in class.

The practical exercises will be implemented directly on a Parrot Ardrone quadrocopter, so we expect a lot of fun (and broken propellors).

Organization

The lecture will be given by Jürgen Sturm.

Lecture: Tuesday, 10:15-11:45, room 02.09.23 (FMI, Boltzmannstrasse 3)

Teaching assistant: Nikolas Engelhard

Lab course/practice: Thursday: 14:15-15:45, room 02.09.23 or 02.09.38 (lab) (FMI, Boltzmannstrasse 3)

Registration: via TUM campus

The oral exam takes place in room 02.09.59. Sign up for a time slot on the list in front of the secretary (room 02.09.52). On the examination day: Please take a seat in the sofa corner in front of room 02.09.52 until we pick you up.

Schedule

A printer ready version (two sides, six pages per sheet) of the lecture notes can be found here: pdf

Date Slides
17.04.2012 Introduction pdf avi (sorry, poor audio quality!)
24.04.2012 Linear algebra, geometry, sensors pdf (bad synchronization + missing end –> use pdf)
08.05.2012 State estimation pdf (bad synchronization –> use pdf)
15.05.2012 Guest talks
22.05.2012 Robot control pdf
05.06.2012 Visual motion estimation pdf
12.06.2012 Simultaneous localization and mapping pdf
19.06.2012 Bundle adjustment and stereo correspondence pdf
26.06.2012 Place recognition, ICP, and dense reconstruction pdf
03.07.2012 Global navigation and path planning pdf
10.07.2012 Planning under uncertainty, exploration and coordination pdf
17.07.2012 Evaluation and benchmarking, time for questions pdf


Recordings can also be found on the TUM TeleTeachingTool website. The raw recordings (before processing) can usually be found a few days before they have been post-processed as MP4 here.

Date Exercise
19.04.2012 Robot lab pdftgz tgz bag1bag2bag3
26.04.2012 Robot lab
03.05.2012 Exercise: Robot Odometry pdf
10.05.2012 Robot lab pdf (Last update: May 14, 12:00)
24.05.2012 Exercise: Robot Localization pdf (Last update: May 24, 21:30)
31.05.2012 Robot lab pdf (Last update: June 4, 12:00)
14.06.2012 Exercise: Position Control pdf
21.06.2012 Exercise: Project proposal pdf pdf
28.06.2012 Robot lab
05.07.2012 Exercise: Project mid-term pdf
12.07.2012 Robot lab
19.07.2012 Exercise: Project presentation pdf pdf

The participation in the exercises is obligatory. Participation in the robot lab is recommended, but not mandatory.

Student Projects

Project title Team name Proposal Midterm Final
Trajectory Generation and Following with Position Correction Crash Pilots pdf pdf pdf
Localization with a particle filter Viking pdf
Autonomous Landing on a Moving Platform Beer pdf pdf pdf
Circling around a person Dragon Sheep pdf pdf
Autonomous flying drone for building surveillance Red Onepdf pdf pdf
Using a Saliency Map to turn the Quadcopter towards interesting points Brezelpdf pdf pdf
Gesture Based Control Weissbier pdf pdf pdf
Fast landing on a moving vehicle Roter Baron pdf pdf pdf
Autonomous Landing on a Moving Platform Weisswurst pdf pdf pdf

The video-taped talks from the final presentations are now available online (password required, same as for ICRA proceedings).

Literature

  • Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT Press, 2005.
  • Computer Vision: Algorithms and Applications. Richard Szeliski. Springer, 2010.

Further Material


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Books | Book Chapters | Journal Articles | Preprints | Conference and Workshop Papers | PhD Thesis | Technical Reports | Other Publications | inproceedings | article
Books
2014
[]Computer Vision: ACCV 2014 (E: D. Cremers, I. Reid, H. Saito and M.-S. Yang), Springer, volume 9003-9007, 2014.  [bibtex]
2013
[]Special Issue: Energy Optimization Methods (GE: Y. Boykov, F. Kahl, V. Lempitsky and F. R. Schmidt), Springer, volume 104, 2013.  [bibtex]
[] Approaches to Probabilistic Model Learning for Mobile Manipulation Robots (J. Sturm), Springer, 2013.  [bibtex] [pdf]
2011
[]Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (E: Y. Boykov, F. Kahl, V. Lempitsky and F. R. Schmidt), Springer, volume 5681, 2011.  [bibtex]
[]Stereoscopic Scene Flow for 3D Motion Analysis (A. Wedel and D. Cremers), Springer, 2011.  [bibtex] [doi]
2010
[]Video Processing and Computational Video (E: D. Cremers, M. Magnor, M. R. Oswald and L. Zelnik-Manor), Springer, volume 7082, 2010.  [bibtex]
2009
[]Statistical and Geometrical Approaches to Visual Motion Analysis (E: D. Cremers, B. Rosenhahn, A. L. Yuille and F. R. Schmidt), Springer, volume 5604, 2009.  [bibtex]
[]Energy Minimization Methods for Computer Vision and Pattern Recognition (EMMCVPR) (E: D. Cremers, Y. Boykov, A. Blake and F. R. Schmidt), Springer, volume 5681, 2009.  [bibtex]
2007
[]Energy Minimization Methods for Computer Vision and Pattern Recognition (EMMCVPR) (E: S.-C. Zhu, A. Yuille, D. Cremers and Y. Wang), Springer, volume 4679, 2007.  [bibtex]
Books | Book Chapters | Journal Articles | Preprints | Conference and Workshop Papers | PhD Thesis | Technical Reports | Other Publications | inproceedings | article
Book Chapters
2020
[]TUM Flyers: Vision—Based MAV Navigation for Systematic Inspection of Structures (V. Usenko, L. von Stumberg, J. Stückler and D. Cremers), Chapter in Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users (F. Caccavale, C. Ott, B. Winkler, Z. Taylor, eds.), Springer International Publishing, 2020.  [bibtex] [doi]
[]On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting (M Brahimi, Y Quéau, B Haefner and D Cremers), Chapter in (JD Durou, M Falcone, Y Quéau, S Tozza, eds.), Springer International Publishing, 2020.  [bibtex] [doi] [arXiv:1911.07268] [pdf]
[] RGB-D Vision (R. Maier and D. Cremers), Chapter in Encyclopedia of Robotics (M.H. Ang, O. Khatib, B. Siciliano, eds.), Springer Berlin Heidelberg, 2020.  [bibtex] [pdf] [doi]
2019
[]A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry (L. von Stumberg, V. Usenko and D. Cremers), Chapter in Multimodal Scene Understanding (M. Yang, B. Rosenhahn, V. Murino, eds.), Academic Press, 2019.  [bibtex] [doi]
2018
[]Image Denoising — Old and New (M Moeller and D Cremers), Chapter in (M Bertalmío, ed.), Springer International Publishing, 2018.  [bibtex] [pdf]
2016
[]Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence (M. Vestner, E. Rodolà, T. Windheuser, RBS. Bulò and D. Cremers), Chapter in Perspectives in Shape Analysis, Springer, 2016.  [bibtex]
2015
[]Holistic Image Reconstruction for Diffusion MRI (V. Golkov, J. M. Portegies, A. Golkov, R. Duits and D. Cremers), Chapter in Computational Diffusion MRI, Springer, 2015.  [bibtex] [pdf]Book Chapter, and Oral Presentation at MICCAI 2015 Workshop on Computational Diffusion MRI
[] Perception of Deformable Objects and Compliant Manipulation for Service Robots (J. Stueckler and S. Behnke), Chapter in Soft Robotics: From Theory to Applications (A. ASO. BA. R A. Verl, ed.), Springer, 2015. (to appear) [bibtex] [pdf]
[]Skeleton-Based Recognition of Shapes in Images via Longest Path Matching (G. Bal, J. Diebold, E. W. Chambers, E. Gasparovic, R. Hu, K. Leonard, M. Shaker and C. Wenk), Chapter in Research in Shape Modeling, Springer International Publishing, volume 1, 2015.  [bibtex] [doi] [pdf]
[]Image Segmentation with Shape Priors: Explicit Versus Implicit Representations (D. Cremers), Chapter in Handbook of Mathematical Methods in Imaging (O. Scherzer, ed.), Springer, 2015.  [bibtex]
2014
[] Active Recognition and Manipulation for Mobile Robot Bin Picking (D. Holz, M. Nieuwenhuisen, D. Droeschel, J. Stueckler, A. Berner, J. Li, R. Klein and S. Behnke), Chapter in Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project, Springer, 2014.  [bibtex] [pdf] [doi]
[] Increasing Flexibility of Mobile Manipulation and Intuitive Human-Robot Interaction in RoboCup@Home (J. Stueckler, D. Droeschel, K. Gräve, D. Holz, M. Schreiber, A. Topaldou-Kyniazopoulou, M. Schwarz and S. Behnke), Chapter in RoboCup 2013, Robot Soccer World Cup XVII, Springer, 2014.  [bibtex] [pdf] [doi]
[]Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate (V. Golkov, J.I. Sperl, M.I. Menzel, T. Sprenger, E.T. Tan, L. Marinelli, C.J. Hardy, A. Haase and D. Cremers), Chapter in Computational Diffusion MRI, Springer, 2014.  [bibtex] [pdf]Book Chapter, and Oral Presentation at MICCAI 2014 Workshop on Computational Diffusion MRI
2013
[] NimbRo@Home: Winning Team of the RoboCup@Home Competition 2012 (J. Stueckler, I. Badami, D. Droeschel, K. Gräve, D. Holz, M. McElhone, M. Nieuwenhuisen, M. Schreiber, M. Schwarz and S. Behnke), Chapter in RoboCup 2012, Robot Soccer World Cup XVI, Springer, 2013.  [bibtex] [pdf] [doi]
[]A Game-Theoretic Approach to Pairwise Clustering and Matching (M. Pelillo, S. R Bulo, A. Torsello, A. Albarelli and E. Rodola), Chapter in Similarity-Based Pattern Analysis and Recognition, Springer, 2013.  [bibtex]
[]Moment Constraints in Convex Optimization for Segmentation and Tracking (M. Klodt, F. Steinbruecker and D. Cremers), Chapter in Advanced Topics in Computer Vision, Springer, 2013.  [bibtex] [pdf]
2012
[] Towards Robust Mobility, Flexible Object Manipulation, and Intuitive Multimodal Interaction for Domestic Service Robots (J. Stueckler, D. Droeschel, K. Gräve, D. Holz, J. Kläß, M. Schreiber, R. Steffens and S. Behnke), Chapter in RoboCup 2011, Robot Soccer World Cup XV, Springer, 2012.  [bibtex] [pdf] [doi]
[]Awareness of Road Scene Participants for Autonomous Driving (A. Petrovskaya, M. Perrollaz, L. Oliveira, L. Spinello, R. Triebel, A. Makris, J.-D. Yoder, U. Nunes, C. Laugier and P. Bessière), Chapter in Handbook of Intelligent Vehicles, Springer, 2012.  [bibtex]
[]A Sequential Monte Carlo Method for Multi-Target Tracking with the Intensity Filter (M. Schikora, W. Koch, R. L. Streit and D. Cremers), Chapter in Advances in Intelligent Signal Processing and Data Mining, Springer-Verlag Berlin Heidelberg, 2012.  [bibtex] [pdf]
[]Body Schema Learning (J. Sturm, C. Plagemann and W. Burgard), Chapter in Towards Service Robots for Everyday Environments, Springer Berlin/Heidelberg, 2012.  [bibtex] [pdf]
2011
[]Image Segmentation with Shape Priors: Explicit Versus Implicit Representations (D. Cremers), Chapter in Handbook of Mathematical Methods in Imaging, Springer, 2011.  [bibtex] [pdf]
[]Convex Relaxation Techniques for Segmentation, Stereo and Multiview Reconstruction (D. Cremers, T. Pock, K. Kolev and A. Chambolle), Chapter in Markov Random Fields for Vision and Image Processing, MIT Press, 2011.  [bibtex] [pdf]
2010
[]An Introduction to Total Variation for Image Analysis (A. Chambolle, V. Caselles, D. Cremers, M. Novaga and T. Pock), Chapter in Theoretical Foundations and Numerical Methods for Sparse Recovery, De Gruyter, 2010.  [bibtex] [pdf]
2007
[]Efficient kernel density estimation of shape and intensity priors for level set segmentation (D. Cremers and M. Rousson), Chapter in Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery (J. S. Suri, A. Farag, eds.), Springer, 2007.  [bibtex] [pdf]
[]Tracking clothed people (B. Rosenhahn, U. Kersting, K. Powell, T. Brox and H. P. Seidel), Chapter in Human Motion - Understanding, Modeling, Capture, and Animation, Springer, 2007.  [bibtex] [pdf]
[]Contours, optic flow, and prior knowledge: cues for capturing 3D human motion in videos (T. Brox, B. Rosenhahn and D. Cremers), Chapter in Human Motion - Understanding, Modeling, Capture, and Animation, Springer, 2007.  [bibtex] [pdf]
2006
[]Diffusion filters and wavelets: What can they learn from each other? (J. Weickert, G. Steidl, P. Mrázek, M. Welk and T. Brox), Chapter in Handbook of Mathematical Models in Computer Vision (N. Paragios, Y. Chen, O. Faugeras, eds.), Springer, 2006.  [bibtex]
[]PDEs for tensor image processing (J. Weickert, C. Feddern, M. Welk, B. Burgeth and T. Brox), Chapter in Visualization and Processing of Tensor Fields (J. Weickert, H. Hagen, eds.), Springer, 2006.  [bibtex]
[]A survey on variational optic flow methods for small displacements (J. Weickert, A. Bruhn, T. Brox and N. Papenberg), Chapter in Mathematical Models for Registration and Applications to Medical Imaging (O. Scherzer, ed.), Springer, volume 10, 2006.  [bibtex]
[]Integral Invariants and Shape Matching (S. Manay, D. Cremers, B. W. Hong, A. Yezzi and S. Soatto), Chapter in Statistical analysis of shapes (modeling and simulation in science, engineering and technology), Birkhauser, 2006.  [bibtex] [pdf]
[]Probabilistic kernel PCA and its application to statistical shape modeling and inference (D. Cremers and T. Kohlberger), Chapter in Kernel Methods in Bioengineering, Signal and Image Processing (G. CVet al., ed.), Idea Group Inc., 2006.  [bibtex]
[]Adaptive structure tensors and their applications (T. Brox, R. van den Boomgaard, F. B. Lauze, J. van de Weijer, J. Weickert, P. Mrázek and P. Kornprobst), Chapter in Visualization and Processing of Tensor Fields (J. Weickert, H. Hagen, eds.), Springer, 2006.  [bibtex]
2005
[]Variational segmentation with shape priors (M. Bergtholdt, D. Cremers and C. Schnörr), Chapter in Handbook of Mathematical Models in Computer Vision (Y. CO. F N. Paragios, ed.), Springer, 2005.  [bibtex]
Books | Book Chapters | Journal Articles | Preprints | Conference and Workshop Papers | PhD Thesis | Technical Reports | Other Publications | inproceedings | article
Journal Articles
2024
[]HI-SLAM: Monocular Real-Time Dense Mapping With Hybrid Implicit Fields (W Zhang, T Sun, S Wang, Q Cheng and N Haala), In IEEE Robotics and Automation Letters (RAL) & Int. Conference on Intelligent Robots and Systems (IROS), volume 9, 2024.  [bibtex] [arXiv:2310.04787]Oral Presentation
[]MaskBit: Embedding-free Image Generation via Bit Tokens (M Weber, L Yu, Q Yu, X Deng, X Shen, D Cremers and LC Chen), In arXiv:2409.16211, 2024. ([project page]) [bibtex]
[]Multi-vehicle trajectory prediction and control at intersections using state and intention information (D Zhu, Q Khan and D Cremers), In Neurocomputing, Elsevier, 2024. ([link][project page][code][pre-print]) [bibtex] [pdf]
2023
[]Uncertainty-driven dense two-view structure from motion (W Chen, S Kumar and F Yu), In IEEE Robotics and Automation Letters (RA-L) and IROS 2023, IEEE, volume 8, 2023.  [bibtex] [arXiv:2302.00523]
[]Semantic Self-adaptation: Enhancing Generalization with a Single Sample (S Bahmani, O Hahn, E Zamfir, N Araslanov, D Cremers and S Roth), In Transactions on Machine Learning Research (TMLR), 2023.  [bibtex] [arXiv:2208.05788]
[]Robust Autonomous Vehicle Pursuit without Expert Steering Labels (J Pan, C Zhou, M Gladkova, Q Khan and D Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 8, 2023. ([arXiv][code]) [bibtex]
[]Learning vision based autonomous lateral vehicle control without supervision (Q Khan, I Sülö, M Öcal and D Cremers), In Applied Intelligence, Springer, 2023. ([paper][github]) [bibtex] [video]
[]E-nerf: Neural radiance fields from a moving event camera (S Klenk, L Koestler, D Scaramuzza and D Cremers), In IEEE Robotics and Automation Letters, IEEE, volume 8, 2023. ([project page]) [bibtex] [pdf]
2022
[]Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in Medical Machine Learning (M Rohr, C Reich, A Höhl, T Lilienthal, T Dege, F Plesinger, V Bulkova, GD Clifford, MA Reyna and C Hoog Antink), In Physiological Measurement, IOP Publishing, volume 43, 2022.  [bibtex]
[]Yeast cell segmentation in microstructured environments with deep learning (T Prangemeier, C Wildner, AO. Françani, C Reich and H Koeppl), In Biosystems, Elsevier, volume 211, 2022.  [bibtex]
[] Deep Learning in Attosecond Metrology (C. Brunner, A. Duensing, C. Schröder, M. Mittermair, V. Golkov, M. Pollanka, D. Cremers and R. Kienberger), In Optics Express, OSA, volume 30, 2022.  [bibtex] [pdf] [doi]Editor's Pick
[]DM-VIO: Delayed Marginalization Visual-Inertial Odometry (L. von Stumberg and D. Cremers), In IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA), volume 7, 2022. ([arXiv][video][project page][supplementary][code]) [bibtex] [doi]
[]A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces (Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers), In International Journal of Computer Vision (IJCV), 2022. ([code]) [bibtex] [doi] [pdf]
[]Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (H Bauermeister, E Laude, T Möllenhoff, M Möller and D Cremers), In SIAM J. Imaging Sci., volume 15, 2022.  [bibtex]
2020
[]Dirac equation on a square waveguide lattice with site-dependent coupling strengths and the gravitational Aharonov-Bohm effect (C Koke, C Noh and D Angelakis), In Physical Review A, volume 102, 2020.  [bibtex] [arXiv:1909.12543]
[] Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing (M. Naeyaert, J. Aelterman, J. Van Audekerke, V. Golkov, D. Cremers, A. Pižurica, J. Sijbers and M. Verhoye), In Magnetic Resonance in Medicine, volume 85, 2020.  [bibtex] [pdf] [doi]
[]ARDEA—An MAV with skills for future planetary missions (P. Lutz, M. G. Müller, M. Maier, S. Stoneman, T. Tomi\´c, I. von Bargen, M. J. Schuster, F. Steidle, A. Wedler, W. Stürzl and R. Triebel), In Journal of Field Robotics (JFR), 2020.  [bibtex]
[]Relocalization With Submaps: Multi-Session Mapping for Planetary Rovers Equipped With Stereo Cameras (R. Giubilato, M. Vayugundla, M. Schuster, W. Stürzl, A. Wedler, R. Triebel and S. Debei), In IEEE Robotics and Automation Letters, volume 5, 2020.  [bibtex] [pdf]
[]From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds (C. Sommer, Y. Sun, L. J. Guibas, D. Cremers and T. Birdal), In IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA), volume 5, 2020.  [bibtex] [doi] [arXiv:2001.07360]
[]GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 5, 2020. ([arXiv][video][project page][supplementary]) [bibtex]
[]Visual-Inertial Mapping with Non-Linear Factor Recovery (V. Usenko, N. Demmel, D. Schubert, J. Stueckler and D. Cremers), In IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robotics and Automation (ICRA), IEEE, volume 5, 2020. ([arxiv]) [bibtex] [doi] [pdf]
[]Photometric Depth Super-Resolution (B. Haefner, S. Peng, A. Verma, Y. Quéau and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), volume 42, 2020. ([supp] [project page]) [bibtex] [doi] [arXiv:1809.10097] [pdf]
[]Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity (E. Laude, P. Ochs and D. Cremers), In Journal of Optimization Theory and Applications, volume 184, 2020.  [bibtex] [arXiv:1907.04306]
2019
[]Spin-Scenario: A flexible scripting environment for realistic MR simulations (Y Chang, D Wei, H Jia, C Curreli, Z Wu, M Sheng, SJ Glaser and X Yang), In Journal of magnetic resonance, Elsevier, volume 301, 2019.  [bibtex]
[]Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection (M. Sundermeyer, Z. Marton, M. Durner and R. Triebel), In International Journal of Computer Vision, 2019.  [bibtex]
[] Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization (F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers and D. Pfeiffer), In Scientific Reports, volume 9, 2019.  [bibtex] [pdf] [doi]
[]A Non-invasive 3D Body Scanner and Software Tool towards Analysis of Scoliosis (S. Roy, A.T.D. Gruenwald, A. Alves-Pinto, R. Maier, D. Cremers, D. Pfeiffer and R. Lampe), In BioMed Research International (BMRI), 2019. ([pdf]) [bibtex]
[]A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking (H Tjaden, U Schwanecke, E Schömer and D Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 41, 2019.  [bibtex] [pdf]
[]Functional Maps Representation on Product Manifolds (E Rodolà, Z Lähner, AM. Bronstein, MM. Bronstein and J Solomon), In Computer Graphics Forum, volume 38, 2019. ((Presented at Symposium on Geometry Processing (SGP)) [arxiv]) [bibtex] [pdf]
[] Video Object Segmentation without Temporal Information (K.-K. Maninis, S. Caelles, Y. Chen, J. PTand L. Leal-Taixé, D. Cremers and L. V Gool), In IEEE Trans. Pattern Anal. Mach. Intell., volume 41, 2019.  [bibtex] [pdf] [doi]
2018
[]Lagrangian Transport Through Surfacesin Compressible Flows (F Hofherr and D Karrasch), In SIAM Journal on Applied Dynamical Systems, SIAM Journal on Applied Mathematics, volume 17, 2018.  [bibtex] [arXiv:1707.00518]
[]Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform (L. Ma, J. Stueckler, T. Wu and D. Cremers), In , 2018. ([arxiv]) [bibtex]
[]Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras (H. Matsuki, L. von Stumberg, V. Usenko, J. Stueckler and D. Cremers), In IEEE Robotics and Automation Letters & Int. Conference on Intelligent Robots and Systems (IROS), IEEE, 2018. ([arxiv]) [bibtex] [pdf]
[]What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? (N Mayer, E Ilg, P Fischer, C Hazirbas, D Cremers, A Dosovitskiy and T Brox), In , volume 41, 2018. (arxiv) [bibtex] [arXiv:1801.06397]
[]Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. (This paper was also selected by ICRA'18 for presentation at the conference.[arxiv][video][code][project]) [bibtex] [pdf]ICRA'18 Best Vision Paper Award - Finalist
[]Variational Reflectance Estimation from Multi-view Images (J. Mélou, Y. Quéau, J.-D. Durou, F. Castan and D. Cremers), In Journal of Mathematical Imaging and Vision, volume 60, 2018. ([arxiv]) [bibtex] [doi] [pdf]
[]The homotopy method revisited: Computing solution paths of L1-regularized problems (B Bringmann, D Cremers and F Krahmer), In Math. Comput., volume 87, 2018.  [bibtex]
[]Variational Methods for Normal Integration (Y. Quéau, J.-D. Durou and J.-F. Aujol), In Journal of Mathematical Imaging and Vision, volume 60, 2018. ([arxiv],[codes]) [bibtex] [doi] [pdf]
[]Normal Integration: A Survey (Y. Quéau, J.-D. Durou and J.-F. Aujol), In Journal of Mathematical Imaging and Vision, volume 60, 2018. ([arxiv],[codes]) [bibtex] [doi] [pdf]
[]LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution (Y. Quéau, B. Durix, T. Wu, D. Cremers, F. Lauze and J.-D. Durou), In Journal of Mathematical Imaging and Vision, volume 60, 2018. ([arxiv],[codes]) [bibtex] [doi] [pdf]
[]Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect (N. Yang, R. Wang, X. Gao and D. Cremers), In In IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robots and Systems (IROS), volume 3, 2018. ([arxiv]) [bibtex] [doi] [pdf]
[]Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.  [bibtex] [pdf]
2017
[]Regularized Pointwise Map Recovery from Functional Correspondence (E Rodolà, M Möller and D Cremers), In Comput. Graph. Forum, volume 36, 2017.  [bibtex]
[]Tau Like Proteins Reduce Torque Generation in Microtubule Bundles (M. Krieg, J. Stühmer, J. G. Cueva, R. Fetter, K. Spilker, D. Cremers, K. Shen, A. R. Dunn and M. B. Goodman), In Biophysical Journal, Elsevier, volume 112, 2017.  [bibtex]
[]Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling (M. Krieg, J. Stühmer, J. G. Cueva, R. Fetter, K. Spilker, D. Cremers, K. Shen, A. R. Dunn and M. B. Goodman), In eLife, eLife Sciences Publications Limited, volume 6, 2017.  [bibtex]
[]Fast and accurate surface normal integration on non-rectangular domains (M. Bähr, M. Breuß, Y. Quéau, A. S. Bouroujerdi and J.-D. Durou), In Computational Visual Media, volume 3, 2017.  [bibtex] [doi] [pdf]
[]Photometric Stereo with Only Two Images: A Theoretical Study and Numerical Resolution (Y. Quéau, R. Mecca, J.-D. Durou and X. Descombes), In Image and Vision Computing, volume 57, 2017.  [bibtex] [doi] [pdf]Editor's choice
[]Consistent Partial Matching of Shape Collections via Sparse Modeling (L. Cosmo, E. Rodola, A. Albarelli, F. Memoli and D. Cremers), In Computer Graphics Forum, Wiley, volume 36, 2017.  [bibtex] [pdf]
[]Partial Functional Correspondence (E. Rodola, L. Cosmo, M. M. Bronstein, A. Torsello and D. Cremers), In Computer Graphics Forum, Wiley, volume 36, 2017.  [bibtex] [pdf]
[]Computer Vision für 3-D-Rekonstruktion - Vom Nischenthema zum Mainstream (D Cremers), In Informatik Spektrum, volume 40, 2017.  [bibtex]
[]Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization (Y. Kee, Y. Lee, M. Souiai, D. Cremers and J. Kim), In SIAM J. Imaging Sci., volume 10, 2017.  [bibtex]
[]Deep Learning for Computer Vision (Dagstuhl Seminar 17391) (D. Cremers, L. Leal-Taixé and R. Vidal), In Dagstuhl Reports, volume 7, 2017.  [bibtex]
[]Spatially Regularized Fusion of Multiresolution Digital Surface Models (G. Kuschk, P. d'Angelo, D. Gaudrie, P. Reinartz and D. Cremers), In IEEE Trans. Geosci. Remote. Sens., volume 55, 2017.  [bibtex]
2016
[]Dirac equation in 2-dimensional curved spacetime, particle creation, and coupled waveguide arrays (C Koke, C Noh and D Angelakis), In Annals of Physics, volume 374, 2016.  [bibtex] [arXiv:1607.04821]
[]A Single-Lobe Photometric Stereo Approach for Heterogeneous Material (R. Mecca, Y. Quéau, F. Logothetis and R. Cipolla), In SIAM Journal on Imaging Sciences, volume 9, 2016.  [bibtex] [doi] [pdf]
[]White Matter MS-Lesion Segmentation Using a Geometric Brain Model (M. Strumia, F. R. Schmidt, C. Anastasopoulos, C. Granziera, G. Krueger and T. Brox), In IEEE Transactions on Medical Imaging, volume 35, 2016.  [bibtex] [pdf]
[]Non-Rigid Puzzles (O. Litany, E. Rodola, A. M. Bronstein, M. M. Bronstein and D. Cremers), In Computer Graphics Forum, Wiley, volume 35, 2016.  [bibtex] [pdf]Received the Best Paper Award at SGP 2016
[]q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann, T. Brox and D. Cremers), In IEEE Transactions on Medical Imaging, volume 35, 2016. Special Issue on Deep Learning [bibtex] [pdf]Special Issue on Deep Learning
[]Bias and Precision Analysis of Diffusional Kurtosis Imaging for Different Acquisition Schemes (T. Sprenger, J. I. Sperl, B. Fernandez, V. Golkov, I. Eidner, P. G. Sämann, M. Czisch, E. T. Tan, C. J. Hardy, L. Marinelli, A. Haase and M. I. Menzel), In Magnetic Resonance in Medicine, 2016. (early view) [bibtex]
[]An Accurate and Robust Artificial Marker based on Cyclic Codes (F. Bergamasco, A. Albarelli, L. Cosmo, E. Rodola and A. Torsello), In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016. (to appear) [bibtex] [pdf]
[]Anisotropic Diffusion Descriptors (D. Boscaini, J. Masci, E. Rodola, M. M. Bronstein and D. Cremers), In Computer Graphics Forum - Proc. EUROGRAPHICS, Wiley, volume 35, 2016.  [bibtex] [pdf]
[]Spectral Decompositions Using One-Homogeneous Functionals (M. Burger, G. Gilboa, M. Möller, L. Eckardt and D. Cremers), In SIAM J. Imaging Sci., volume 9, 2016.  [bibtex]
[]Collaborative Total Variation: A General Framework for Vectorial TV Models (J. Duran, M. Möller, C. Sbert and D. Cremers), In SIAM J. Imaging Sci., volume 9, 2016.  [bibtex] [pdf]
[] Midrange Geometric Interactions for Semantic Segmentation (J. Diebold, C. Nieuwenhuis and D. Cremers), In International Journal of Computer Vision, Springer US, volume 117, 2016. Special Issue on Graphical Models for Scene Understanding [bibtex] [pdf] [doi] [pdf]
2015
[]Variational Depth From Focus Reconstruction (M. Möller, M. Benning, C. Schönlieb and D. Cremers), In IEEE Trans. Image Process., volume 24, 2015.  [bibtex]
[]Incremental and Batch Planar Simplification of Dense Point Cloud Maps (T. Whelan, L. Ma, E. Bondarev, P. de With and J. McDonald), In Robotics and Autonomous Systems (RAS) ECMR '13 Special Issue, North-Holland Publishing Co., volume 69, 2015.  [bibtex] [pdf] [video]
[]Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications (Y. Kee, H. Lee, J. Yim, D. Cremers and J. Kim), In IEEE Signal Process. Lett., volume 22, 2015.  [bibtex]
[]Introspective classification for robot perception (H Grimmett, R Triebel, R Paul and I Posner), In The International Journal of Robotics Research (IJRR), 2015.  [bibtex] [pdf]
[]Cloud-based collaborative 3D mapping in real-time with low-cost robots (G. Mohanarajah, V. Usenko, M. Singh, R. D'Andrea and M. Waibel), In IEEE Transactions on Automation Science and Engineering, IEEE, volume 12, 2015.  [bibtex] [pdf]
[]The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings (T. Möllenhoff, E. Strekalovskiy, M. Möller and D. Cremers), In SIAM Journal on Imaging Sciences, volume 8, 2015.  [bibtex] [pdf]
[]Realistic Photometric Stereo Using Partial Differential Irradiance Equation Ratios (R. Mecca, E. Rodola and D. Cremers), In Computers and Graphics, Elsevier, volume 51, 2015.  [bibtex] [doi] [pdf]
[]A Simple and Effective Relevance-based Point Sampling for 3D Shapes (E. Rodola, A. Albarelli, D. Cremers and A. Torsello), In Pattern Recognition Letters, Elsevier, volume 59, 2015.  [bibtex] [pdf] [code]
[]Multi-Layered Mapping and Navigation for Autonomous Micro Aerial Vehicles (D. Droeschel, M. Nieuwenhuisen, M. Beul, J. Stueckler, D. Holz and S. Behnke), In Journal of Field Robotics, 2015. (to appear) [bibtex]
[]NimbRo Explorer: Semi-Autonomous Exploration and Mobile Manipulation in Rough Terrain (J. Stueckler, M. Schwarz, M. Schadler, A. Topalidou-Kyniazopoulou and S. Behnke), In Journal of Field Robotics, 2015. (to appear) [bibtex]
[] Efficient Dense Rigid-Body Motion Segmentation and Estimation in RGB-D Video (J. Stueckler and S. Behnke), In International Journal of Computer Vision, Springer US, 2015.  [bibtex] [pdf] [doi]
[]Efficient Reactive Navigation with Exact Collision Determination for 3D Robot Shapes (J. L. BC M. Jaimez and J. Gonzalez-Jimenez), In International Journal of Advanced Robotic Systems, volume 12, 2015. ([video]) [bibtex] [pdf]
[]Fast Visual Odometry for 3-D Range Sensors (M. Jaimez and J. Gonzalez-Jimenez), In IEEE Transactions on Robotics, volume 31, 2015. ([video]) [bibtex] [pdf]
[]Field phenotyping of grapevine growth using dense stereo reconstruction (M. Klodt, K. Herzog, R. Töpfer and D. Cremers), In BMC Bioinformatics, volume 16, 2015.  [bibtex]
[]Car detection by fusion of HOG and causal MRF (S. Madhogaria, P. M. Baggenstoss, M. Schikora, W. Koch and D. Cremers), In IEEE T. on Aerospace and Electronic Systems, volume 51, 2015.  [bibtex]
[]The Role of Diffusion in Figure Hunt Games (J. Diebold, S. Tari and D. Cremers), In Journal of Mathematical Imaging and Vision, Springer, volume 52, 2015.  [bibtex] [doi] [pdf]
[]Fast and Accurate Surface Alignment through an Isometry-Enforcing Game (A. Albarelli, E. Rodola and A. Torsello), In Pattern Recognition, Elsevier, volume 48, 2015.  [bibtex] [doi] [pdf]
2014
[] Multi-Resolution Surfel Maps for Efficient Dense 3D Modeling and Tracking (J. Stueckler and S. Behnke), In Journal of Visual Communication and Image Representation, volume 25, 2014.  [bibtex] [pdf] [doi]
[] Dense Real-Time Mapping of Object-Class Semantics from RGB-D Video (J. Stueckler, B. Waldvogel, H. Schulz and S. Behnke), In Journal of Real-Time Image Processing, Springer, 2014.  [bibtex] [pdf] [doi]
[] Rough Terrain Mapping and Navigation using a Continuously Rotating 2D Laser Scanner (M. Schadler, J. Stueckler and S. Behnke), In Künstliche Intelligenz, Springer, volume 28, 2014.  [bibtex] [pdf] [doi]
[]Robust Region Detection via Consensus Segmentation of Deformable Shapes (E. Rodola, S. R Bulo and D. Cremers), In Computer Graphics Forum, Wiley, volume 33, 2014.  [bibtex] [pdf] [code]
[]Scale-Aware Navigation of a Low-Cost Quadrocopter with a Monocular Camera (J. Engel, J. Sturm and D. Cremers), In Robotics and Autonomous Systems (RAS), volume 62, 2014.  [bibtex] [pdf]
[]Convex Relaxation of Vectorial Problems with Coupled Regularization (E. Strekalovskiy, A. Chambolle and D. Cremers), In SIAM Journal on Imaging Sciences, volume 7, 2014.  [bibtex] [pdf]
[]A Super-resolution Framework for High-Accuracy Multiview Reconstruction (B. Goldluecke, M. Aubry, K. Kolev and D. Cremers), In International Journal of Computer Vision, volume 106, 2014.  [bibtex] [pdf]
2013
[]Introduction to the special issue on visual understanding and applications with RGB-D cameras (Z. Liu, M. Beetz, D. Cremers, J. Gall, W. Li, D. Pangercic, J. Sturm and Y.-W. Tai), In Journal of Visual Communication and Image Representation (JVCI), 2013.  [bibtex]
[]3D Mapping with an RGB-D Camera (F. Endres, J. Hess, J. Sturm, D. Cremers and W. Burgard), In IEEE Transactions on Robotics (T-RO), volume 30, 2013.  [bibtex] [pdf]
[]Tight Convex Relaxations for Vector-Valued Labeling (B. Goldluecke, E. Strekalovskiy and D. Cremers), In SIAM Journal on Imaging Sciences, volume 6, 2013.  [bibtex] [pdf]
[]A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model (C. Nieuwenhuis, E. Toeppe and D. Cremers), In International Journal of Computer Vision, volume 104, 2013. (Code available) [bibtex] [pdf]
[]Spatially Varying Color Distributions for Interactive Multi-Label Segmentation (C. Nieuwenhuis and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 35, 2013. (Code available) [bibtex] [pdf]
[]Stable and Fast Techniques for Unambiguous Compound Phase Coding (A. Torsello, A. Albarelli and E. Rodola), In Image and Vision Computing, volume 31, 2013.  [bibtex] [doi] [pdf]
[]A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes (E. Rodola, A. Albarelli, F. Bergamasco and A. Torsello), In International Journal of Computer Vision, Springer US, volume 102, 2013.  [bibtex] [doi] [pdf]
2012
[] RoboCup@Home: Demonstrating Everyday Manipulation Skills in RoboCup@Home (J. Stueckler, R. Steffens, D. Holz and S. Behnke), In IEEE Robotics and Automation Magazine, volume 19, 2012.  [bibtex] [pdf] [doi]
[] Efficient 3D Object Perception and Grasp Planning for Mobile Manipulation in Domestic Environments (J. Stueckler, R. Steffens, D. Holz and S. Behnke), In Robotics and Autonomous Systems, volume 61, 2012.  [bibtex] [pdf] [doi]
[]Total Cyclic Variation and Generalizations (D. Cremers and E. Strekalovskiy), In Journal of Mathematical Imaging and Vision, volume 47, 2012.  [bibtex] [pdf]
[]An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium (M. Schikora, B. Neupane, S. Madhogaria, W. Koch, D. Cremers, H. Hirt, K.-H. Kogel and A. Schikora), In BMC Bioinformatics, volume 13, 2012.  [bibtex] [pdf]
[]Online-6D-SLAM für RGB-D-Sensoren (F. Endres, J. Hess, N. Engelhard, J. Sturm and W. Burgard), In at - Automatisierungstechnik, volume 60, 2012.  [bibtex] [pdf]
[]Fast Joint Estimation of Silhouettes and Dense 3D Geometry from Multiple Images (K. Kolev, T. Brox and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 34, 2012.  [bibtex] [pdf]
[]On the André motive of certain irreducible symplectic varieties (U. Schlickewei), In Geometriae Dedicata, volume 156, 2012.  [bibtex] [pdf]
[]The Natural Total Variation Which Arises from Geometric Measure Theory (B. Goldluecke, E. Strekalovskiy and D. Cremers), In SIAM Journal on Imaging Sciences, volume 5, 2012.  [bibtex] [pdf]
[]Image segmentation with one shape prior - A template-based formulation (S. Chen, D. Cremers and R. J. Radke), In Image and Vision Computing, volume 30, 2012.  [bibtex] [pdf]
[]Optimal Solutions for Semantic Image Decomposition (D. Cremers), In Image and Vision Computing, volume 30, 2012.  [bibtex] [pdf]
[]A linear framework for region-based image segmentation and inpainting involving curvature penalization (T. Schoenemann, F. Kahl, S. Masnou and D. Cremers), In International Journal of Computer Vision, volume 99, 2012.  [bibtex] [pdf]
[]A Coding Cost Framework for Super-resolution Motion Layer Decomposition (T. Schoenemann and D. Cremers), In IEEE Transactions on Image Processing, volume 21, 2012.  [bibtex] [pdf]
[]A Convex Approach to Minimal Partitions (A. Chambolle, D. Cremers and T. Pock), In SIAM Journal on Imaging Sciences, volume 5, 2012.  [bibtex] [pdf]
[]Imposing Semi-local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: a Game-Theoretic Perspective (A. Albarelli, E. Rodola and A. Torsello), In International Journal of Computer Vision, Springer US, volume 97, 2012.  [bibtex] [doi] [pdf]Invited submission
2011
[]Bootstrap Optical Flow and Uncertainty Measure (J. Kybic and C. Nieuwenhuis), In Computer Vision and Image Understanding, volume 115, 2011.  [bibtex] [pdf]
[]A Probabilistic Framework for Learning Kinematic Models of Articulated Objects (J. Sturm, C. Stachniss and W. Burgard), In Journal on Artificial Intelligence Research (JAIR), volume 41, 2011.  [bibtex] [pdf]
[]A Variational Approach to Vesicle Membrane Reconstruction from Fluorescence Imaging (K. Kolev, N. Kirchgessner, S. Houben, A. Csiszar, W. Rubner, C. Palm, B. Eiben, R. Merkel and D. Cremers), In Pattern Recognition, volume 44, 2011.  [bibtex] [pdf]
[]Tactile Sensing for Mobile Manipulation (S. Chitta, J. Sturm, M. Piccoli and W. Burgard), In IEEE Transactions on Robotics (T-RO), 2011.  [bibtex] [pdf]
[]Motion Field Estimation from Alternate Exposure Images (A. Sellent, M. Eisemann, B. Goldluecke, D. Cremers and M. Magnor), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, 2011.  [bibtex] [pdf]
[]The Elastic Ratio: Introducing Curvature into Ratio-Based Globally Optimal Image Segmentation (T. Schoenemann, S. Masnou and D. Cremers), In IEEE Transactions on Image Processing, volume 20, 2011.  [bibtex] [pdf]
[]Stereoscopic Scene Flow Computation for 3D Motion Understanding (A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke and D. Cremers), In International Journal of Computer Vision, volume 95, 2011.  [bibtex] [pdf]
[]Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains (D. Cremers and K. Kolev), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, 2011.  [bibtex] [pdf]
[]Large-Scale Integer Linear Programming for Orientation-Preserving 3D Shape Matching (T. Windheuser, U. Schlickewei, F. R. Schmidt and D. Cremers), In Computer Graphics Forum (Proceedings Symposium Geometry Processing), Eurographics, volume 30, 2011.  [bibtex] [pdf]
2010
[]Multiclass Multimodal Detection and Tracking in Urban Environments (L.Spinello, R. Triebel and R. Siegwart), In International Journal of Robotics Research, 2010.  [bibtex] [pdf]
[]Movement Directionality in Collective Migration of Germ Layer Progenitors (Y. Arboleda-Estudillo, M. Krieg, J. Stühmer, N. A. Licata, D. J. Muller and C.-P. Heisenberg), In Current Biology, volume 20, 2010.  [bibtex]
[]Stability of tautological vector bundles on Hilbert squares of surfaces (U. Schlickewei), In Rendiconti del Seminario Matematico della Universitá di Padova, volume 124, 2010.  [bibtex] [pdf]
[]The Hodge conjecture for self-products of certain K3 surfaces (U. Schlickewei), In Journal of Algebra, volume 324, 2010.  [bibtex] [pdf]
[]A Combinatorial Solution for Model-based Image Segmentation and Real-time Tracking (T. Schoenemann and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, 2010.  [bibtex] [pdf]
[]Global Solutions of Variational Models with Convex Regularization (T. Pock, D. Cremers, H. Bischof and A. Chambolle), In SIAM Journal on Imaging Sciences, volume 3, 2010.  [bibtex] [pdf]
2009
[]Control of convergent yolk syncytial layer nuclear movement in zebrafish (L. Carvalho, J. Stühmer, J. S. Bois, Y. Kalaidzidis, V. Lecaudey and C. P. Heisenberg), In Development, volume 136, 2009.  [bibtex]
[] An appearance-based visual compass for mobile robots (J. Sturm and A. Visser), In Robotics and Autonomous Systems, volume 57, 2009.  [bibtex] [pdf] [doi] [pdf]
[] Body schema learning for robotic manipulators from visual self-perception (J. Sturm, C. Plagemann and W. Burgard), In Journal of Physiology-Paris, volume 103, 2009. (Neurorobotics) [bibtex] [pdf] [doi] [pdf]
[]Folgen von Höhenfußpunktdreiecken und ihre Grenzpunkte (E. Strekalovskiy), In Elemente der Mathematik, volume 64, 2009.  [bibtex]
[]Hodge classes on self-products of K3 surfaces (U. Schlickewei), In Bonner Mathematische Schriften, volume 395, 2009. (Ph.D. thesis) [bibtex] [pdf]
[]B-Spline Modeling of Road Surfaces with an Application to Free Space Estimation (A. Wedel, C. Rabe, H. Badino, H. Loose, U. Franke and D. Cremers), In Transactions on Intelligent Transportation Systems, volume 10, 2009.  [bibtex] [pdf]
[]Continuous Global Optimization in Multiview 3D Reconstruction (K. Kolev, M. Klodt, T. Brox and D. Cremers), In International Journal of Computer Vision, volume 84, 2009.  [bibtex] [pdf]
[]Combined region- and motion-based 3D tracking of rigid and articulated objects (T. Brox, B. Rosenhahn, J. Gall and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, 2009.  [bibtex] [pdf]
[]On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional (T. Brox and D. Cremers), In International Journal of Computer Vision, volume 84, 2009.  [bibtex] [pdf]
2008
[] Hierarchical Reactive Control for Humanoid Soccer Robots (S. Behnke and J. Stueckler), In International Journal of Humanoid Robots, volume 5, 2008.  [bibtex] [pdf]
[]Monte Carlo localization in outdoor terrains using multilevel surface maps (R. Kümmerle, R. Triebel, P.Pfaff and W. Burgard), In Journal of Field Robotics, volume 25, 2008.  [bibtex] [pdf]
[]Tailoring the magnetoresistance of MnAs/GaAs:Mn granular hybrid nanostructures (C. Michel, M. T. Elm, B. Goldluecke, S. D. Baranovskii, P. Thomas, W. Heimbrodt and P. J. Klar), In Applied Physics Letters, volume 92, 2008.  [bibtex] [pdf]
[]Influence of non-random incorporation of Mn ions on the magnetotransport properties of $Ga_{1-x}Mn_{x}As$ alloys (C. Michel, M. T. Elm, S. D. Baranovskii, P. Thomas, W. Heimbrodt, B. Goldluecke and P. J. Klar), In Physica status solidi. C. Current topics in solid state physics, volume 5, 2008.  [bibtex] [pdf]
[]3-D Reconstruction of Shaded Objects from Multiple Images Under Unknown Illumination (H. Jin, D. Cremers, D. Wang, A. Yezzi, E. Prados and S. Soatto), In International Journal of Computer Vision, volume 76, 2008.  [bibtex] [pdf]
[]Nonlinear Dynamical Shape Priors for Level Set Segmentation (D. Cremers), In Journal of Scientific Computing, volume 35, 2008.  [bibtex] [pdf]
[]Efficient Nonlocal Means for Denoising of Textural Patterns (T. Brox, O. Kleinschmidt and D. Cremers), In IEEE Transactions on Image Processing, volume 17, 2008.  [bibtex] [pdf]
2007
[]Non-iterative Vision-based Interpolation of 3D Laser Scans (H. Andreasson, R. Triebel and A. Lilienthal), In Autonomous Robots and Agents, volume 76, 2007.  [bibtex] [pdf]
[]An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing (P. Pfaff, R. Triebel and W. Burgard), In International Journal of Robotics Research (IJRR), volume 26, 2007.  [bibtex] [pdf]
[]Supervised semantic labeling of places using information extracted from sensor data (Ó. M Mozos, R. Triebel, P. Jensfelt, A. Rottmann and W. Burgard), In Journal on Robotics and Autonomous Systems (RAS), volume 55, 2007.  [bibtex] [pdf]
[]Quantitative modeling of the annealing-induced changes of the magnetotransport in $Ga_{1-x}Mn_{x}As$ alloys (C. Michel, S. D. Baranovskii, P. Thomas, W. Heimbrodt, M. T. Elm, P. J. Klar, B. Goldluecke, U. Wurstbauer, M. Reinwald and W. Wegscheider), In Journal of Applied Physics, volume 102, 2007.  [bibtex] [pdf]
[]Weighted Minimal Hypersurface Reconstruction (B. Goldluecke, I. Ihrke, C. Linz and M. Magnor), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 29, 2007.  [bibtex] [pdf]
[]A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape (D. Cremers, M. Rousson and R. Deriche), In International Journal of Computer Vision, volume 72, 2007.  [bibtex] [pdf]
[]Computer Lernen Sehen (D. Cremers), In Industrial Vision, volume 2, 2007.  [bibtex] [pdf]
[]Three-dimensional shape knowledge for joint image segmentation and pose tracking (B. Rosenhahn, T. Brox and J. Weickert), In International Journal of Computer Vision, volume 73, 2007. (available online) [bibtex]
[]Algorithmic Differentiation: Application to Variational Problems in Computer Vision (T. Pock, M. Pock and H. Bischof), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 29, 2007.  [bibtex]
2006
[]Strong non-Arrhenius temperature dependence of the resistivity in the regime of traditional band transport (C. Michel, S. D. Baranovskii, P. J. Klar, P. Thomas and B. Goldluecke), In Applied Physics Letters, volume 89, 2006.  [bibtex] [pdf]
[]Integral invariants for shape matching (S. Manay, D. Cremers, B.-W. Hong, A. Yezzi and S. Soatto), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
[]A multiphase dynamic labeling model for variational recognition-driven image segmentation (D. Cremers, N. Sochen and C. Schnörr), In International Journal of Computer Vision, volume 66, 2006.  [bibtex] [pdf]
[]Kernel density estimation and intrinsic alignment for shape priors in level set segmentation (D. Cremers, S. J. Osher and S. Soatto), In International Journal of Computer Vision, volume 69, 2006.  [bibtex] [pdf]
[]Dynamical statistical shape priors for level set based tracking (D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
2005
[]Spin-dependent localization effects in GaAs:Mn/MnAs granular paramagnetic-ferromagnetic hybrids at low temperatures (C. Michel, C.H. Thien, S. Ye, P.J. Klar, W. Heimbrodt, S.D. Baranovskii, P. Thomas, M. Lampalzer, K. Volz, W. Stolz and B. Goldluecke), In J. Superlattices and Microstructures, Elsevier, volume 37, 2005.  [bibtex] [pdf]
[]Motion Competition: A variational framework for piecewise parametric motion segmentation (D. Cremers and S. Soatto), In International Journal of Computer Vision, volume 62, 2005.  [bibtex] [pdf]
2003
[]Binary partitioning, perceptual grouping, and restoration with semidefinite programming (J. Keuchel, C. Schnörr, C. Schellewald and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 25, 2003.  [bibtex] [pdf]
[]Statistical shape knowledge in variational motion segmentation (D. Cremers and C. Schnörr), In Image and Vision Computing, volume 21, 2003.  [bibtex] [pdf]
[]Shape Statistics in Kernel Space for Variational Image Segmentation (D. Cremers, T. Kohlberger and C. Schnörr), In Pattern Recognition, volume 36, 2003.  [bibtex] [pdf]Awarded Best Paper of the Year 2003
2002
[]Diffusion Snakes: Introducing statistical shape knowledge into the Mumford–Shah functional (D. Cremers, F. Tischhäuser, J. Weickert and C. Schnörr), In International Journal of Computer Vision, volume 50, 2002.  [bibtex] [pdf]
[]Travelling waves of exitation in neural field models: Equivalence of rate descriptions and integrate-and-fire dynamics (D. Cremers and A. V. M. Herz), In Neural Computation, volume 14, 2002.  [bibtex] [pdf]
1999
[]Flow equations for the Héon-Heiles Hamiltonian (D. Cremers and A. Mielke), In Physica D, volume 126, 1999.  [bibtex] [pdf]
Books | Book Chapters | Journal Articles | Preprints | Conference and Workshop Papers | PhD Thesis | Technical Reports | Other Publications | inproceedings | article
Preprints
2024
[]Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining (Y Ma, A Li, Q Khan and D Cremers), In arXiv preprint arXiv:2409.05119, 2024.  [bibtex]
[]GlORIE-SLAM: Globally Optimized Rgb-only Implicit Encoding Point Cloud SLAM (G Zhang, E Sandström, Y Zhang, M Patel, L Van Gool and MR Oswald), In arXiv preprint arXiv:2403.19549, 2024. ([project],[code]) [bibtex] [arXiv:2403.19549]
[]VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition (YJ Li, M Gladkova, Y Xia, R Wang and D Cremers), In arXiv preprint arXiv:2403.14594, 2024.  [bibtex] [arXiv:2403.14594]
[] How to Choose a Reinforcement-Learning Algorithm (F Bongratz, V Golkov, L Mautner, L Della Libera, F Heetmeyer, F Czaja, J Rodemann and D Cremers), In arXiv preprint arXiv:2407.20917, 2024.  [bibtex] [pdf] [arXiv:2407.20917]
[]Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations (C Tomani, K Chaudhuri, I Evtimov, D Cremers and M Ibrahim), In arXiv preprint, 2024.  [bibtex] [arXiv:2404.10960]
2023
[]Deep Event Visual Odometry (S Klenk, M Motzet, L Koestler and D Cremers), In arXiv preprint arXiv:2312.09800, 2023.  [bibtex]
[]Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution (12-page version) (HN Dang, V Golkov, T Wimmer, D Cremers, A Maier and M Zaiss), In arXiv preprint arXiv:2305.07524, 2023.  [bibtex] [arXiv:2305.07524]
[]Scale-Equivariant Deep Learning for 3D Data (T Wimmer, V Golkov, HN Dang, M Zaiss, A Maier and D Cremers), In arXiv preprint, 2023.  [bibtex] [arXiv:2304.05864] [pdf]
2022
[]4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions (P Wenzel, N Yang, R Wang, N Zeller and D Cremers), In arXiv preprint arXiv:2301.01147, 2022.  [bibtex] [arXiv:2301.01147]
[]Implicit Shape Completion via Adversarial Shape Priors (A Saroha, M Eisenberger, T Yenamandra and D Cremers), In arXiv preprint arXiv:2204.10060, 2022.  [bibtex]
[]Challenger: Training with Attribution Maps (C Tomani and D Cremers), In arXiv preprint, 2022.  [bibtex] [arXiv:2205.15094]
2021
[]DeepLab2: A TensorFlow Library for Deep Labeling (M Weber, H Wang, S Qiao, J Xie, MD Collins, Y Zhu, L Yuan, D Kim, Q Yu, D Cremers and others), In arXiv preprint arXiv:2106.09748, 2021.  [bibtex]
[]Scene Graph Generation for Better Image Captioning? (M. Mozes, M. Schmitt, V. Golkov, H. Schütze and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2109.11398] [pdf]
[]Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization (J. Chui, S. Klenk and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2107.04536] [pdf]
[]Rotation-Equivariant Deep Learning for Diffusion MRI (P. Müller, V. Golkov, V. Tomassini and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2102.06942] [pdf]
2020
[]Neural Online Graph Exploration (I Chiotellis and D Cremers), In arXiv preprint arXiv:2012.03345, 2020. ([arxiv]) [bibtex]
[]Speech Synthesis and Control Using Differentiable DSP (G Fabbro, V Golkov, T Kemp and D Cremers), In arXiv preprint arXiv:2010.15084, 2020. ([listen to audio results]) [bibtex] [arXiv:2010.15084] [pdf]
[]Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions (V. Golkov, A. Becker, D. T. Plop, D. Čuturilo, N. Davoudi, J. Mendenhall, R. Moretti, J. Meiler and D. Cremers), In arXiv preprint arXiv:2007.07029, 2020.  [bibtex] [arXiv:2007.07029] [pdf]
2019
[]Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods (L. Della Libera, V. Golkov, Y. Zhu, A. Mielke and D. Cremers), In arXiv preprint arXiv:1910.14594, 2019.  [bibtex] [arXiv:1910.14594] [pdf]
[]Learning to Evolve (J. Schuchardt, V. Golkov and D. Cremers), In arXiv preprint arXiv:1905.03389, 2019.  [bibtex] [arXiv:1905.03389] [pdf]
2018
[]Clustering with Deep Learning: Taxonomy and New Methods (E. Aljalbout, V. Golkov, Y. Siddiqui, M. Strobel and D. Cremers), In arXiv preprint arXiv:1801.07648, 2018.  [bibtex] [arXiv:1801.07648]
[]Precursor microRNA Identification Using Deep Convolutional Neural Networks (B. T. Do, V. Golkov, G. E. Gürel and D. Cremers), In bioRxiv preprint 414656, 2018. (bioRxiv:414656) [bibtex] [pdf]
2017
[]Regularization for Deep Learning: A Taxonomy (J. Kukačka, V. Golkov and D. Cremers), In arXiv preprint arXiv:1710.10686, 2017.  [bibtex] [arXiv:1710.10686] [pdf]
2016
[]Bayesian Inference of Bijective Non-Rigid Shape Correspondence (M. Vestner, R. Litman, A. Bronstein, E. Rodola and D. Cremers), In arXiv preprint arXiv:1607.03425, 2016. ([slides]) [bibtex] [pdf]
2007
[]Fully automated segmentation and morphometrical analysis of muscle fibre images (Y.-J. Kim, T. Brox, W. Feiden and J. Weickert), In Cytometry Part A, volume 71, 2007. (available online) [bibtex]
2006
[]Numerical aspects of TV flow (M. Breuß, T. Brox, A. Bürgel, T. Sonar and J. Weickert), In Numerical Algorithms, volume 41, 2006.  [bibtex]
[]Nonlinear structure tensors (T. Brox, J. Weickert, B. Burgeth and P. Mrázek), In Image and Vision Computing, volume 24, 2006.  [bibtex]
[]Highly accurate optic flow computation with theoretically justified warping (N. Papenberg, A. Bruhn, T. Brox, S. Didas and J. Weickert), In International Journal of Computer Vision, volume 67, 2006.  [bibtex]
[]A TV flow based local scale estimate and its application to texture discrimination (T. Brox and J. Weickert), In Journal of Visual Communication and Image Representation, volume 17, 2006.  [bibtex]
[]Level Set Segmentation with Multiple Regions (T. Brox and J. Weickert), In IEEE Transactions on Image Processing, volume 15, 2006.  [bibtex]
2004
[]On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs (G. Steidl, J. Weickert, T. Brox, P. Mrázek and M. Welk), In SIAM Journal on Numerical Analysis, volume 42, 2004.  [bibtex] [pdf]
Books | Book Chapters | Journal Articles | Preprints | Conference and Workshop Papers | PhD Thesis | Technical Reports | Other Publications | inproceedings | article
Conference and Workshop Papers
2025
[]On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches (F Hofherr, B Haefner and D Cremers), In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. (to appear) [bibtex]
2024
[]Variational Low-Rank Adaptation Using IVON (B Cong, N Daheim, Y Shen, D Cremers, R Yokota, ME Khan and T Möllenhoff), In NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability, 2024.  [bibtex]
[]Interactions Across Blocks in Post-Training Quantization of LLMs (K Shabanovi, L Wiest, T Pfeil, V Golkov and D Cremers), In NeurIPS Workshop on Machine Learning and Compression, 2024.  [bibtex]
[]Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments (L Cheng, J Hu, H Yan, M Gladkova, T Huang, YH Liu, D Cremers and H Li), In International Conference on Intelligent Robots and Systems (IROS), 2024.  [bibtex] [arXiv:2409.11854]
[]CARLA Drone: Monocular 3D Object Detection from a Different Perspective (JM Meier, L Scalerandi, O Dhaouadi, J Kaiser, N Araslanov and D Cremers), In 46th German Conference on Pattern Recognition (GCPR), 2024. ([project page]) [bibtex]Oral Presentation
[]An Image is Worth 32 Tokens for Reconstruction and Generation (Q Yu, M Weber, X Deng, X Shen, D Cremers and LC Chen), In NeurIPS, 2024. ([project page]) [bibtex]
[]MeshFeat: Multi-Resolution Features for Neural Fields on Meshes (M Mahajan, F Hofherr and D Cremers), In European Conference on Computer Vision (ECCV), 2024. ([project page]) [bibtex] [arXiv:2407.13592]
[]Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians (S Qian, T Kirschstein, L Schoneveld, D Davoli, S Giebenhain and M Niessner), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. ( [project] ) [bibtex] [arXiv:2312.02069]
[]DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting (L Härenstam-Nielsen, L Sang, A Saroha, N Araslanov and D Cremers), In European Conference on Computer Vision (ECCV), 2024.  [bibtex] [arXiv:2407.17058]
[]Partial-to-Partial Shape Matching with Geometric Consistency (V Ehm, M Gao, P Roetzer, M Eisenberger, D Cremers and F Bernard), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([webpage]) [bibtex]
[]SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation (A Toker, M Eisenberger, D Cremers and L Leal-Taixe), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.  [bibtex]
[]Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation (D Cao, M Eisenberger, N El Amrani, D Cremers and F Bernard), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.  [bibtex]
[]MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation (L Yang, L Hoyer, M Weber, T Fischer, D Dai, L Leal-Taixé, D Cremers, M Pollefeys and LV Gool), In European Conference on Computer Vision (ECCV), 2024.  [bibtex]
[]Variational Learning is Effective for Large Deep Networks (Y Shen, N Daheim, B Cong, P Nickl, GM Marconi, C Bazan, R Yokota, I Gurevych, D Cremers, ME Khan and T Möllenhoff), In International Conference on Machine Learning (ICML), 2024. ([code][blog][tutorial]) [bibtex] [arXiv:2402.17641]Spotlight
[]Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment (S Weber, JH Hong and D Cremers), In European Conference on Computer Vision (ECCV), 2024.  [bibtex] [arXiv:2405.05079]
[] Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization (Z Ye, G Peyré, D Cremers and P Ablin), In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (S Dasgupta, S Mandt, Y Li, eds.), PMLR, volume 238, 2024.  [bibtex] [pdf]
[]Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo (M Brahimi, B Haefner, Z Ye, B Goldluecke and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([supp]) [bibtex] [pdf]
[]ControlRoom3D: Room Generation using Semantic Proxy Rooms (J Schult, S Tsai, L Höllein, B Wu, J Wang, CY Ma, K Li, X Wang, F Wimbauer, Z He, P Zhang, B Leibe, P Vajda and J Hou), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.  [bibtex]
[]From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers (S Gurumurthy, K Ram, B Chen, Z Manchester and Z Kolter), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.  [bibtex]
[]Leveraging neural radiance fields for uncertainty-aware visual localization (L Chen, W Chen, R Wang and M Pollefeys), In IEEE International Conference on Robotics and Automation (ICRA), 2024.  [bibtex] [arXiv:2310.06984]
[]LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry (W Chen, L Chen, R Wang and M Pollefeys), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page]) [bibtex] [arXiv:2401.01887]
[]Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation (K Han, D Muhle, F Wimbauer and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page]) [bibtex] [arXiv:2404.07933]
[]Gaussian Splatting in Style (A Saroha, M Gladkova, C Curreli, D Muhle, T Yenamandra and D Cremers), In German Conference on Pattern Recognition (GCPR), 2024.  [bibtex]
[]Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball (S Weber, B Zöngür, N Araslanov and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page][video]) [bibtex] [arXiv:2404.03778]
[]Finsler-Laplace-Beltrami Operators with Application to Shape Analysis (S Weber, T Dagès, M Gao and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page][video]) [bibtex] [arXiv:2404.03999]
[]A Perspective on Deep Vision Performance with Standard Image and Video Codecs (C Reich, O Hahn, D Cremers, S Roth and B Debnath), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024.  [bibtex] [arXiv:2404.12330]
[]Deep Video Codec Control (C Reich, B Debnath, D Patel, T Prangemeier, D Cremers and S Chakradhar), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024.  [bibtex] [arXiv:2308.16215]
[]Differentiable JPEG: The Devil is in the Details (C Reich, B Debnath, D Patel and S Chakradhar), In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. ([project page]) [bibtex] [arXiv:2309.06978]
[]An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment (S Solonets, D Sinitsyn, L von Stumberg, N Araslanov and D Cremers), In International Conference on Learning Representations (ICLR), 2024.  [bibtex]Oral Presentation
[]Geometrically Consistent Partial Shape Matching (V Ehm, P Roetzer, M Eisenberger, M Gao, F Bernard and D Cremers), In 3DV, 2024.  [bibtex]
[]Cache Me if You Can: Accelerating Diffusion Models through Block Caching (F Wimbauer, B Wu, E Schoenfeld, X Dai, J Hou, Z He, A Sanakoyeu, P Zhang, S Tsai, J Kohler, C Rupprecht, D Cremers, P Vajda and J Wang), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page]) [bibtex] [arXiv:2312.03209]
[]S4C: Self-Supervised Semantic Scene Completion with Neural Fields (A Hayler, F Wimbauer, D Muhle, C Rupprecht and D Cremers), In 2024 International Conference on 3D Vision (3DV), 2024. ([project page]) [bibtex] [arXiv:2310.07522] [pdf]
[]Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining (U Sahin, H Li, Q Khan, D Cremers and T Volker), In IEEE Winter Conference on Applications of Computer Vision (WACV, 2024. ([arXiv][project page][code]) [bibtex]
[]Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography (D Komorowicz, L Sang, F Maiwald and D Cremers), In German Conference on Pattern Recognition (GCPR), 2024. ([project page]) [bibtex] [arXiv:2311.17810]
[]Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis (MS Deka, L Sang and D Cremers), In German Conference on Pattern Recognition (GCPR), 2024. ([project page]) [bibtex] [arXiv:2311.17634]
[] Transferability for Graph Convolutional Networks (C Koke, A Saroha, Y Shen, M Eisenberger, MM. Bronstein and D Cremers), In ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024.  [bibtex] [pdf]Outstanding Extended Abstract
[]HoloNets: Spectral Convolutions do extend to Directed Graphs (C Koke and D Cremers), In International Conference on Learning Representations (ICLR), 2024.  [bibtex] [arXiv:2310.02232]
[]Text2Loc: 3D Point Cloud Localization from Natural Language (Y Xia, L Shi, Z Ding, JF Henriques and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page][code]) [bibtex]
[]Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations (L Sang, A Saroha, M Gao and D Cremers), In ECCV workshop: wild in 3D, 2024. ([project page]) [bibtex] [arXiv:2306.02099]
[]Improving the Detection of Air-Voids and Aggregates in Images of Concrete Using Generative AI (Q Khan, M Hassan, V Kostic, V Golkov, C Gehlen and D Cremers), In GNI Symposium on AI for the Built World, 2024.  [bibtex]
[]The Pulseq-CEST Library: Definition of Preparations and Simulations, Example Data, and Example Evaluations (A Liebeskind, MS Fabian, JR Schüre, S Weinmüller, P Schünke, V Golkov, D Cremers and M Zaiss), In 10th International Workshop on Chemical Exchange Saturation Transfer (CEST 2024), 2024.  [bibtex]Oral Presentation
[]Joint sequence optimization beats pure neural network approaches for super-resolution TSE (HN Dang, V Golkov, J Endres, S Weinmüller, F Glang, T Wimmer, D Cremers, A Dörfler, A Maier and and M Zaiss), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2024.  [bibtex]
[]Masked Event Modeling: Self-Supervised Pretraining for Event Cameras (S Klenk, D Bonello, L Koestler, N Araslanov and D Cremers), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2024.  [bibtex] [arXiv:2212.10368] [pdf]
[]FIRe: Fast Inverse Rendering Using Directional and Signed Distance Functions (T Yenamandra, A Tewari, N Yang, F Bernard, C Theobalt and D Cremers), In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. ([Project page],[ArXiv]) [bibtex]
[]Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model (C Tomani, D Vilar, M Freitag, C Cherry, S Naskar, M Finkelstein, X Garcia and D Cremers), In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.  [bibtex] [arXiv:2310.06707]
[]SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering (M Brahimi, B Haefner, T Yenamandra, B Goldluecke and D Cremers), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2024. ([supp]) [bibtex] [arXiv:2212.04968] [pdf]
2023
[]Revisiting Rotation Averaging: Uncertainties and Robust Losses (G Zhang, V Larsson and D Barath), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([code]) [bibtex] [arXiv:2303.05195]
[]Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes (G Eskandar, Y Farag, T Yenamandra, D Cremers, K Guirguis and B Yang), In 2023 IEEE Intelligent Vehicles Symposium (IV), 2023.  [bibtex]
[]To adapt or not to adapt? Real-time adaptation for semantic segmentation (MB Colomer, PL Dovesi, T Panagiotakopoulos, JF Carvalho, L Härenstam-Nielsen, H Azizpour, H Kjellström, D Cremers and M Poggi), In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.  [bibtex]
[]Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks (O Wysocki, Y Xia, M Wysocki, E Grilli, L Hoegner, D Cremers and U Stilla), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.  [bibtex]
[]SCP: Scene Completion Pre-training for 3D Object Detection (Y Shan, Y Xia, Y Chen and D Cremers), In ISPRS Geospatial Week, 2023.  [bibtex]Best paper award
[]DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates (H Li, J Dong, B Wen, M Gao, T Huang, YH Liu and D Cremers), In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.  [bibtex]
[]Artificial Intelligence for the Automated Creation of Multi-scale Digital Twins of the Built World—AI4TWINNING (A Borrmann, M Biswanath, A Braun, Z Chen, D Cremers, M Heeramaglore, L Hoegner, M Mehranfar, TH Kolbe, F Petzold and others), In International 3D GeoInfo Conference, 2023.  [bibtex]
[]Transformer Network with Time Prior for Predicting Clinical Outcome from EEG of Cardiac Arrest Patients (M Rohr, T Schilke, L Willems, C Reich, S Dill, G Güney and C Hoog Antink), In 50th Computing in Cardiology Conference (CinC), 2023.  [bibtex]
[]On the Atrial Fibrillation Detection Performance of ECG-DualNet (C Reich, M Rohr, T Kircher and C Hoog Antink), In 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-Page Paper, medRxiv, 2023.  [bibtex]
[]The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures (C Reich, T Prangemeier and H Koeppl), In IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023. ([project page]) [bibtex] [arXiv:2304.07597]
[]An Instance Segmentation Dataset of Yeast Cells in Microstructures (C Reich, T Prangemeier, AO Françani and H Koeppl), In 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. ([project page]) [bibtex] [arXiv:2304.07597]
[]SIGMA: Quantum Scale-Invariant Global Sparse Shape Matching (M Gao, P Roetzer, M Eisenberger, Z Lähner, M Moeller, D Cremers and F Bernard), In International Conference on Computer Vision (ICCV), 2023. ([pdf]) [bibtex]
[]Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks (D Schnaus, J Lee, D Cremers and R Triebel), In International Conference on Machine Learning, 2023.  [bibtex] [pdf]
[]ResolvNet: A Graph Convolutional Network with multi-scale Consistency (C Koke, A Saroha, Y Shen, M Eisenberger and D Cremers), In NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023.  [bibtex] [arXiv:2310.00431]Oral Presentation
[]HoloNets: Spectral Convolutions do extend to Directed Graphs (C Koke and D Cremers), In NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023.  [bibtex] [arXiv:2310.02232]Oral Presentation
[]CASSPR: Cross Attention Single Scan Place Recognition (Y Xia, M Gladkova, R Wang, Q Li, U Stilla, JF. Henriques and D Cremers), In IEEE International Conference on Computer Vision (ICCV), 2023. ([code]) [bibtex] [arXiv:2211.12542]
[]Multi Agent Navigation in Unconstrained Environments Using a Centralized Attention Based Graphical Neural Network Controller (Y Ma, Q Khan and D Cremers), In IEEE 26th International Conference on Intelligent Transportation Systems, 2023. ([project page][code]) [bibtex] [pdf]
[]LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels (J Schmidt, Q Khan and D Cremers), In IEEE 26th International Conference on Intelligent Transportation Systems, 2023. ([project page][arxiv][code]) [bibtex]
[]Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares (D Muhle, L Koestler, KM Jatavallabhula and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. ([project page]) [bibtex] [arXiv:2305.09527] [pdf]
[]Non-Separable Multi-Dimensional Network Flows for Visual Computing (V Ehm, D Cremers and F Bernard), In Eurographics 2023 - Posters, The Eurographics Association, 2023.  [bibtex] [doi]
[] GPT4MR: Exploring GPT-4 as an MR Sequence and Reconstruction Programming Assistant (M Zaiss, HN Dang, V Golkov, J Rajput, D Cremers, F Knoll and A Maier), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2023.  [bibtex] [pdf]Oral Presentation
[]Limitless Stability for Graph Convolutional Networks (C Koke), In International Conference on Learning Representations (ICLR), 2023.  [bibtex] [arXiv:2301.11443]
[]NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging (K Guirguis, J Meier, G Eskandar, M Kayser, B Yang and J Beyerer), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.  [bibtex] [arXiv:2303.04958]
[]Behind the Scenes: Density Fields for Single View Reconstruction (F Wimbauer, N Yang, C Rupprecht and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([project page]) [bibtex] [arXiv:2301.07668]
[]Power Bundle Adjustment for Large-Scale 3D Reconstruction (S Weber, N Demmel, T Chon Chan and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([project page][video]) [bibtex] [arXiv:2204.12834] [pdf]
[]Semidefinite Relaxations for Robust Multiview Triangulation (L Härenstam-Nielsen, N Zeller and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.  [bibtex] [arXiv:2301.11431]
[]High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF (L Sang, B Haefner, X Zuo and D Cremers), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2023. ([code] [project page]) [bibtex] [arXiv:2210.12202]Spotlight Presentation
[]Neural Implicit Representations for Physical Parameter Inference from a Single Video (F Hofherr, L Koestler, F Bernard and D Cremers), In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. ([project page]) [bibtex] [arXiv:2204.14030]
[]Beyond In-Domain Scenarios: Robust Density-Aware Calibration (C Tomani, F Waseda, Y Shen and D Cremers), In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.  [bibtex] [arXiv:2302.05118]
[]G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors (M. Eisenberger, A. Toker, L. Leal-Taixé and D. Cremers), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([arXiv] [code]) [bibtex]
2022
[]Vision-Based Large-scale 3D Semantic Mapping for Autonomous Driving Applications (Q Cheng, N Zeller and D Cremers), In International Conference on Robotics and Automation (ICRA), 2022.  [bibtex] [arXiv:2203.01087]
[]UNIF: United neural implicit functions for clothed human reconstruction and animation (S Qian, J Xu, Z Liu, L Ma and S Gao), In European Conference on Computer Vision, 2022. ( [project] ) [bibtex] [arXiv:2207.09835]
[]Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels (AG Gul, O Cetin, C Reich, N Flinner, T Prangemeier and H Koeppl), In Medical Imaging 2022: Digital and Computational Pathology, Proceedings of SPIE, volume 12039, 2022.  [bibtex] [arXiv:2210.09021]
[]A scalable combinatorial solver for elastic geometrically consistent 3d shape matching (P Roetzer, P Swoboda, D Cremers and F Bernard), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.  [bibtex] [pdf]
[]Graph Scattering beyond Wavelet Shackles (C Koke and G Kutyniok), In Neural Information Processing Systems Conference (NeurIPS), 2022.  [bibtex] [arXiv:2301.11456]
[]A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs (HHH Hsu, Y Shen and D Cremers), In NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022. ([code]) [bibtex] [arXiv:2210.15575]
[]Deep Combinatorial Aggregation (Y Shen and D Cremers), In NeurIPS, 2022. ([code][blog]) [bibtex] [arXiv:2210.06436]
[]What Makes Graph Neural Networks Miscalibrated? (HHH Hsu, Y Shen, C Tomani and D Cremers), In NeurIPS, 2022. ([code]) [bibtex] [arXiv:2210.06391]
[]DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment (M Gladkova, N Korobov, N Demmel, A Ošep, L Leal-Taixé and D Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2022. ([project page]) [bibtex] [arXiv:2209.14965]
[]Challenges of SLAM in extremely unstructured environments: the DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset (R Giubilato, W Stürzl, A Wedler and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]The Probabilistic Robot Kinematics Model and its Application to Sensor Fusion (L Meyer, KH. Strobl and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]Bayesian Active Learning for Sim-to-Real Robotic Perception (J Feng, J Lee, M Durner and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]A Two-stage Learning Architecture that Generates High-Quality Grasps for a Multi-Fingered Hand (D Winkelbauer, B Bäuml, N Thuerey and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]RECALL: Rehearsal-free Continual Learning for Object Classification (M Knauer, M Denninger and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]Towards Safety-Aware Pedestrian Detection in Autonomous Systems (M Lyssenko, CD Gladisch, C Heinzemann, M Woehrle and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2022.  [bibtex]
[]Towards Robust Perception of Unknown Objects in the Wild (W Boerdijk, M Durner, M Sundermeyer and R Triebel), In ICRA Workshop on Robotic Perception and Mapping: Emerging Techniques, 2022.  [bibtex]
[]Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects (M Stoiber, M Sundermeyer and R Triebel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  [bibtex]
[]Seeking Visual Discomfort: Curiosity-Driven Representations for Reinforcement Learning (E Aljalbout, M Ulmer and R Triebel), In International Conference on Robotics and Automation (ICRA), 2022.  [bibtex]
[]A Model for Multi-View Residual Covariances Based on Perspective Deformation (AF Villacampa, LO Maza, J Civera and R Triebel), In International Conference on Robotics and Automation (ICRA), 2022.  [bibtex]
[]Intrinsic Neural Fields: Learning Functions on Manifolds (L Koestler, D Grittner, M Moeller, D Cremers and Z Lähner), In European Conference on Computer Vision (ECCV), 2022. (Code will be released soon.) [bibtex] [arXiv:2203.07967] [pdf]
[]Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation (D Das, Q Khan and D Cremers), In IEEE International Conference on Image Processing, 2022.  [bibtex] [pdf]
[]Biologically Inspired Neural Path Finding (L Hang, Q Khan, V Tresp and D Cremers), In Brain Informatics, Springer International Publishing, 2022. ([code]) [bibtex]
[]Lateral Ego-Vehicle Control Without Supervision Using Point Clouds (F Müller, Q Khan and D Cremers), In Pattern Recognition and Artificial Intelligence, Springer International Publishing, 2022.  [bibtex] [pdf]
[]De-rendering 3D Objects in the Wild (F Wimbauer, S Wu and C Rupprecht), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  [bibtex] [arXiv:2201.02279]
[]The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions (D Muhle, L Koestler, N Demmel, F Bernard and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. ([project page]) [bibtex] [arXiv:2204.02256] [pdf]
[]Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections (Z Ye, T Yenamandra, F Bernard and D Cremers), In AAAI, 2022. ([supp]) [bibtex] [pdf]
[] Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction (C Sommer, L Sang, D Schubert and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. ([poster] [presentation] [code]) [bibtex] [pdf]
[]A data-driven variability assessment of brain diffusion MRI preprocessing pipelines (J. Veraart and 100 coauthors), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2022.  [bibtex] [pdf]Oral Presentation
[]Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration (C Tomani, D Cremers and F Buettner), In European Conference on Computer Vision (ECCV), 2022.  [bibtex] [arXiv:2102.12182]
[]A Unified Framework for Implicit Sinkhorn Differentiation (M. Eisenberger, A. Toker, L. Leal-Taixé, F. Bernard and D. Cremers), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. ([arXiv] [code]) [bibtex]
[]DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation (A Toker, L Kondmann, M Weber, M Eisenberger, C Andres, J Hu, A Hoderlein, C Senaras, T Davis, D Cremers, G Marchisio, X Zhu and L Leal-Taixe), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  [bibtex] [pdf]
2021
[]Speech drives templates: Co-speech gesture synthesis with learned templates (S Qian, Z Tu, Y Zhi, W Liu and S Gao), In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. ( [project] ) [bibtex] [arXiv:2108.08020]
[]OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data (C Reich, T Prangemeier, Ö Cetin and H Koeppl), In British Machine Vision Conference (BMVC), 2021. ([project page]) [bibtex] [arXiv:2110.10640]
[]Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy (C Reich, T Prangemeier, C Wildner and H Koeppl), In International Conference on Medical image computing and computer-assisted intervention (MICCAI), 2021. ([project page]) [bibtex] [arXiv:2106.08285]
[]4D Panoptic LiDAR Segmentation (M Aygun, A Osep, M Weber, M Maximov, C Stachniss, J Behley and L Leal-Taixé), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [bibtex]
[]STEP: Segmenting and Tracking Every Pixel (M Weber, J Xie, M Collins, Y Zhu, P Voigtlaender, H Adam, B Green, A Geiger, B Leibe, D Cremers and others), In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Track on Datasets and Benchmarks), volume 1, 2021.  [bibtex]
[]A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines (V Borisov, J Meier, Jvan den Heuvel, H Jalali and G Kasneci), In Neural Information Processing Systems Conference - NeurIPS 2021: eXplainable AI approaches for debugging and diagnosis workshop, 2021.  [bibtex] [arXiv:2111.07379]
[]Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis (V Ehm, D Cremers and F Bernard), In 2021 International Conference on 3D Vision (3DV), 2021.  [bibtex]
[]Explicit pairwise factorized graph neural network for semi-supervised node classification (Y Wang, Y Shen and D Cremers), In UAI, 2021. ([code]) [bibtex] [arXiv:2107.13059]
[]A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments (MG Müller, M Durner, A Gawel, W Stürzl, R Triebel and R Siegwart), In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.  [bibtex]
[]Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes (M Lyssenko, C Gladisch, C Heinzemann, M Woehrle and R Triebel), In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE, 2021.  [bibtex]
[]Unknown Object Segmentation from Stereo Images (M Durner, W Boerdijk, M Sundermeyer, W Friedl, ZC Marton and R Triebel), In International Conference on Intelligent Robots and Systems, 2021.  [bibtex]
[]Multi-Modal Loop Closing in Unstructured Planetary Environments with Visually Enriched Submaps (R Giubilato, M Vayugundla, W Stürzl, M Schuster, A Wedler and R Triebel), In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.  [bibtex]
[]Learning-Based Matching of 3D Submaps from Dense Stereo for Planetary-Like Environments (HC Liao, R Giubilato, W Stürzl and R Triebel), In International Conference on Advanced Robotics (ICAR), 2021.  [bibtex]
[]Kronecker-Factored Optimal Curvature (D Schnaus, J Lee and R Triebel), In Bayesian Deep Learning NeurIPS 2021 Workshop, 2021. ([poster]) [bibtex] [pdf]
[]Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes (J Lee, J Feng, M Humt, MG Müller and R Triebel), In 5th Conference on Robot Learning (CoRL), 2021.  [bibtex]
[]Multidirectional Conjugate Gradients for Scalable Bundle Adjustment (S Weber, N Demmel and D Cremers), In German Conference on Pattern Recognition (GCPR), 2021. ([presentation]) [bibtex] [pdf]
[]TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo (L Koestler, N Yang, N Zeller and D Cremers), In Conference on Robot Learning (CoRL), 2021. ([GitHub][video][project page]) [bibtex] [arXiv:2111.07418] [pdf]3DV'21 Best Demo Award
[]TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset (S Klenk, J Chui, N Demmel and D Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2021. ([project page]) [bibtex] [arXiv:2108.07329] [pdf]
[]Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions (MW Wudenka, MG Müller, N Demmel, A Wedler, R Triebel, D Cremers and W Stuerzl), In International Conference on Intelligent Robots and Systems (IROS), 2021. ([code][video]) [bibtex] [arXiv:2109.05509] [pdf]
[]Square Root Marginalization for Sliding-Window Bundle Adjustment (N Demmel, D Schubert, C Sommer, D Cremers and V Usenko), In IEEE International Conference on Computer Vision (ICCV), 2021. ([project page]) [bibtex] [arXiv:2109.02182] [pdf]
[]From Evaluation to Verification: Towards Task-Oriented Relevance Metrics for Pedestrian Detection in Safety-Critical Domains (M Lyssenko, C Gladisch, C Heinzemann, M Woehrle and R Triebel), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021.  [bibtex] [pdf]
[]Post-hoc Uncertainty Calibration for Domain Drift Scenarios (C Tomani, S Gruber, ME Erdem, D Cremers and F Buettner), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [bibtex] [arXiv:2012.10988]Oral Presentation
[]Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration (C Tomani and F Buettner), In InThirty-FifthAAAIConferenceonArtificialIntelligence(AAAI-2021), 2021.  [bibtex] [arXiv:2012.10923]
[]Square Root Bundle Adjustment for Large-Scale Reconstruction (N Demmel, C Sommer, D Cremers and V Usenko), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page]) [bibtex] [arXiv:2103.01843] [pdf]
[]DOT: Dynamic Object Tracking for Visual SLAM (I Ballester, A Fontan, J Civera, KH. Strobl and R Triebel), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.  [bibtex]
[]Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes (M Sundermeyer, A Mousavian, R Triebel and D Fox), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.  [bibtex]
[]"What's This?" - Learning to Segment Unknown Objects from Manipulation Sequences (W Boerdijk, M Sundermeyer, M Durner and R Triebel), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.  [bibtex]
[]Exploration of Large Outdoor Environments Using Multi-Criteria Decision Making (H Lehner, MJ. Schuster, T Bodenmüller and R Triebel), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.  [bibtex]
[]Learning to Localize in New Environments from Synthetic Training Data (D Winkelbauer, M Denninger and R Triebel), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.  [bibtex]
[]Robust Approaches for Localization on Multi-camera Systems in Dynamic Environments (M Sewtz, X Luo, J Landgraf, T Bodenmüller and R Triebel), In Proceedings of the IEEE International Conference on Automation, Robotics and Applications(ICARA), 2021.  [bibtex]
[]Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning (P. Wenzel, T. Schön, L. Leal-Taixé and D. Cremers), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021. ([arXiv]) [bibtex]
[]SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition (Y. Xia, Y. Xu, S. Li, R. Wang, J. Du, D. Cremers and U. Stilla), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arxiv] [code]) [bibtex]Oral Presentation
[]Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry (M Gladkova, R Wang, N Zeller and D Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2021. ([project page]) [bibtex] [arXiv:2102.01191]
[]Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry (Q. Khan, P. Wenzel and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. ([arXiv]) [bibtex]
[]Rotation-Equivariant Deep Learning for Diffusion MRI (short version) (P. Müller, V. Golkov, V. Tomassini and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021.  [bibtex] [pdf]
[]Faster and better HARDI using FSE and holistic reconstruction (M Naeyaert, V Golkov, D Cremers, J Sijbers and M Verhoye), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021.  [bibtex] [pdf]
[]Isometric Multi-Shape Matching (M Gao, Z Lähner, J Thunberg, D Cremers and F Bernard), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arxiv]) [bibtex]Oral Presentation
[]i3DMM: Deep Implicit 3D Morphable Model of Human Heads (T Yenamandra, A Tewari, F Bernard, HP Seidel, M Elgharib, D Cremers and C Theobalt), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([Project page],[ArXiv]) [bibtex]Oral Presentation
[]MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page]) [bibtex] [arXiv:2011.11814]
[]Sublabel-Accurate Multilabeling Meets Product Label Spaces (Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers), In DAGM German Conference on Pattern Recognition (GCPR), 2021. ([presentation] [code]) [bibtex] [doi] [pdf]Oral Presentation
[]Bregman Proximal Gradient Algorithms for Deep Matrix Factorization (M. C. Mukkamala, F. Westerkamp, E. Laude, D. Cremers and P. Ochs), In Scale Space and Variational Methods in Computer Vision (A Elmoataz, J Fadili, Y Quéau, J Rabin, L Simon, eds.), Springer International Publishing, 2021.  [bibtex] [arXiv:1910.03638]
[]NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go (M. Eisenberger, D. Novotny, G. Kerchenbaum, P. Labatut, N. Neverova, D. Cremers and A. Vedaldi), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arXiv] [code]) [bibtex]
[]Variational Data Assimilation with a Learned Inverse Observation Operator (T Frerix, D Kochkov, J Smith, D Cremers, M Brenner and S Hoyer), In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. (URL, code) [bibtex]
[]Recovering Real-world Reflectance Properties and Shading from HDR Imagery (B. Haefner, S. Green, A. Oursland, D. Andersen, M. Goesele, D. Cremers, R. Newcombe and T. Whelan), In International Conference on 3D Vision (3DV), 2021. ([supp] [slides] [poster] [FB Research] [video]) [bibtex] [doi] [pdf]Spotlight Presentation
2020
[]Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning (T Prangemeier, C Wildner, AO. Françani, C Reich and H Koeppl), In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020.  [bibtex] [arXiv:2011.08062]
[]Attention-Based Transformers for Instance Segmentation of Cells in Microstructures (T Prangemeier, C Reich and H Koeppl), In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.  [bibtex] [arXiv:2011.09763]
[]4D generic video object proposals (A Ošep, P Voigtlaender, M Weber, J Luiten and B Leibe), In International Conference on Robotics and Automation (ICRA), 2020.  [bibtex]
[]Single-Shot Panoptic Segmentation (M Weber, J Luiten and B Leibe), In International Conference on Intelligent Robots and Systems (IROS), 2020.  [bibtex]
[]Optimal least-squares solution to the hand-eye calibration problem (A Dekel, L Härenstam-Nielsen and S Caccamo), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [arXiv:2002.10838]
[]LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization (L. von Stumberg, P. Wenzel, N. Yang and D. Cremers), In International Conference on 3D Vision (3DV), 2020. ([arXiv][project page][video][supplementary][poster]) [bibtex]
[]Distributed Photometric Bundle Adjustment (N Demmel, M Gao, E Laude, T Wu and D Cremers), In International Conference on 3D Vision (3DV), 2020. ([project page][code]) [bibtex] [pdf]Oral Presentation
[]Robust Vision-Based Pose Correction for a Robotic Manipulator using Active Markers (L Meyer, K Strobl and R Triebel), In 17th International Symposium on Experimental Robotics (ISER), 2020. (to appear) [bibtex]
[]A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking (M Stoiber, M Pfanne, K Strobl, R Triebel and A Albu-Schaeffer), In Asian Conference on Computer Vision, 2020. () [bibtex]Best Paper Award
[]Incremental learning of EMG-based control commands using Gaussian Processes (F Schiel, A Hagengruber, J Vogel and R Triebel), In Conference on Robot Learning (CoRL), 2020. () [bibtex] [pdf]
[]Self-Supervised Object-in-Gripper Segmentation from Robotic Motions (W Boerdijk, M Sundermeyer, M Durner and R Triebel), In Conference on Robot Learning (CoRL), 2020. () [bibtex]
[]Unsupervised Dense Shape Correspondence using Heat Kernels (M Aygün, Z Lähner and D Cremers), In International Conference on 3D Vision (3DV), 2020. ([arxiv]) [bibtex] [pdf]
[]Simulated Annealing for 3D Shape Correspondence (B Holzschuh, Z Lähner and D Cremers), In International Conference on 3D Vision (3DV), 2020.  [bibtex] [pdf]Oral Presentation
[]4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving (P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller and D. Cremers), In Proceedings of the German Conference on Pattern Recognition (GCPR), 2020. ([project page][arXiv][video]) [bibtex] [pdf]
[]Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels (L. Koestler, N. Yang, R. Wang and D. Cremers), In Proceedings of the German Conference on Pattern Recognition (GCPR), 2020. ([project page][video]) [bibtex] [pdf]
[]PrimiTect: Fast Continuous Hough Voting for Primitive Detection (C. Sommer, Y. Sun, E. Bylow and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2020.  [bibtex] [doi] [arXiv:2005.07457]
[]Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments (CL Gentil, M Vayugundla, R Giubilato, W Stürzl, TA. Vidal-Calleja and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2020. (to appear) [bibtex]
[]Robust MUSIC-Based Sound Source Localization in Reverberant and Echoic Environments (M Sewtz, T Bodenmüller and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2020. (to appear) [bibtex]
[]DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization (J. Du, R. Wang and D. Cremers), In European Conference on Computer Vision (ECCV), 2020. ([project page][code][supplementary][arxiv]) [bibtex] [pdf]Spotlight Presentation
[]3D Scene Reconstruction from a Single Viewport (M Denninger and R Triebel), In European Conference on Computer Vision (ECCV), 2020.  [bibtex] [pdf]
[]Effective Version Space Reduction for Convolutional Neural Networks (J Liu, I Chiotellis, R Triebel and D Cremers), In European Conference on Machine Learning and Data Mining (ECML-PKDD), 2020. ([arxiv]) [bibtex] [pdf]
[]Estimating Model Uncertainty of Neural Networks in Sparse Information Form (J Lee, M Humt, J Feng and R Triebel), In International Conference on Machine Learning (ICML), 2020.  [bibtex] [pdf]
[]Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning (Z. Ye, T. Möllenhoff, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. ([code]) [bibtex] [pdf]
[]Visual-Inertial Telepresence for Aerial Manipulation (J. Lee, R. Balachandran, Y. Sarkisov, M. D Stefano, A. Coelho, K. Shinde, M. J. Kim, R. Triebel and K. Kondak), In International Conference on Robotics and Automation (ICRA), 2020.  [bibtex] [pdf]
[]Non-Parametric Calibration for Classification (J. Wenger, H. Kjellström and R. Triebel), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.  [bibtex] [pdf]
[]Multi-path Learning for Object Pose Estimation Across Domains (M. Sundermeyer, M. Durner, E. Y. Puang, Z.-C. Marton, N. Vaskevicius, K. O. Arras and R. Triebel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [pdf]
[]Information-Driven Direct RGB-D Odometry (A. Fontan-Villacampa, J. Civera and R. Triebel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [pdf]Oral Presentation
[]D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (N. Yang, L. von Stumberg, R. Wang and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [arXiv:2003.01060] [pdf]Oral Presentation
[]Efficient Derivative Computation for Cumulative B-Splines on Lie Groups (C. Sommer, V. Usenko, D. Schubert, N. Demmel and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [doi] [arXiv:1911.08860] [pdf]Oral Presentation
[]Correspondence-Free Material Reconstruction using Sparse Surface Constraints (S. Weiss, R. Maier, D. Cremers, R. Westermann and N. Thuerey), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [pdf]
[]Deep Shells: Unsupervised Shape Correspondence with Optimal Transport (M. Eisenberger, A. Toker, L. Leal-Taixé and D. Cremers), In 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. ([arXiv] [code]) [bibtex]
[]Hamiltonian Dynamics for Real-World Shape Interpolation (M. Eisenberger and D. Cremers), In European Conference on Computer Vision (ECCV), 2020. ([arXiv] [code]) [bibtex]Spotlight Presentation
[]Smooth Shells: Multi-Scale Shape Registration with Functional Maps (M. Eisenberger, Z. Lähner and D. Cremers), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020. ([pdf] [arXiv] [code]) [bibtex]Oral Presentation
[]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020. ([video][presentation][project page][supplementary][arxiv]) [bibtex] [pdf]
[]Homogeneous Linear Inequality Constraints for Neural Network Activations (T Frerix, M Nießner and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020. (URL, code) [bibtex]
[]Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach (L. Sang, B. Haefner and D. Cremers), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2020. ([poster] [presentation] [code] [cvf]) [bibtex] [doi] [arXiv:1912.06501] [pdf]Spotlight Presentation
[]3D Deep Learning for Biological Function Prediction from Physical Fields (V. Golkov, M. J. Skwark, A. Mirchev, G. Dikov, A. R. Geanes, J. Mendenhall, J. Meiler and D. Cremers), In International Conference on 3D Vision (3DV), 2020.  [bibtex] [arXiv:1704.04039] [pdf]
2019
[]Visual person understanding through multi-task and multi-dataset learning (K Pfeiffer, A Hermans, I Sárándi, M Weber and B Leibe), In German Conference on Pattern Recognition (GCPR), 2019.  [bibtex]
[]Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks (J. Feng, M. Durner, Z.-C. Marton, F. Balint-Benczedi and R. Triebel), In International Symposium on Robotics Research (ISRR), 2019.  [bibtex] [pdf]
[]Visual-inertial sensor fusion with a bio-inspired polarization compass for navigation of MAVs (F. Steidle, W. Stürzl and R. Triebel), In 11th International Micro Air Vehicle Competition and Conference (IMAV), 2019.  [bibtex]
[]Informative GANs via Structured Regularization of Optimal Transport (P. Bréchet, T. Wu, T. Möllenhoff and D. Cremers), In NeurIPS Workshop on Optimal Transport and Machine Learning, 2019.  [bibtex] [arXiv:1912.02160]
[]Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction (S. Weiss, R. Maier, R. Westermann, D. Cremers and N. Thuerey), In arXiv preprint arXiv:1910.01812, 2019. ([pdf]) [bibtex] [arXiv:1910.01812]
[]Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light (E. Jung, N. Yang and D. Cremers), In Conference on Robot Learning (CoRL), 2019. ([arxiv],[supplementary],[video]) [bibtex]Full Oral Presentation
[]Controlling Neural Networks via Energy Dissipation (M. Moeller, T. Möllenhoff and D. Cremers), In International Conference on Computer Vision (ICCV), 2019.  [bibtex] [arXiv:1904.03081]
[]Visual Repetition Sampling for Robot Manipulation Planning (E.Y. Puang, P. Lehner, Z.C. Marton, M. Durner, R. Triebel and A. Albu-Schäffer), In International Conference on Robotics and Automation (ICRA), 2019.  [bibtex] [pdf]
[]Towards Generalizing Sensorimotor Control Across Weather Conditions (Q. Khan, P. Wenzel, D. Cremers and L. Leal-Taixé), In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. ([arXiv]) [bibtex] [pdf]
[]Approximating Orthogonal Matrices with Effective Givens Factorization (T. Frerix and J. Bruna), In Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. (URL, code) [bibtex]
[]Flat Metric Minimization with Applications in Generative Modeling (T. Möllenhoff and D. Cremers), In International Conference on Machine Learning (ICML), 2019. (arXiv:1905.04730, code, talk) [bibtex]Full Oral Presentation
[]Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus (T. Möllenhoff and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (arXiv:1905.00851, talk) [bibtex] [pdf]Oral Presentation
[]Optimization of Inf-Convolution Regularized Nonconvex Composite Problems (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.  [bibtex] [pdf]
[]Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models (E. Bylow, R. Maier, F. Kahl and C. Olsson), In Scandinavian Conference on Image Analysis (SCIA), 2019. ([slides] [poster]) [bibtex] [pdf]Oral Presentation, received the SCIA 2019 Honourable Mention award
[]Divergence-Free Shape Correspondence by Deformation (M. Eisenberger, Z. Lähner and D. Cremers), In Computer Graphics Forum, volume 38, 2019. ([arxiv]) [bibtex] [pdf]
[]Rolling-Shutter Modelling for Visual-Inertial Odometry (D. Schubert, N. Demmel, L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2019. ([arxiv]) [bibtex] [pdf]
[]Negative-Unlabeled Learning for Diffusion MRI (P. Swazinna, V. Golkov, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2019.  [bibtex] [pdf]
[]q-Space Novelty Detection with Variational Autoencoders (A. Vasilev, V. Golkov, M. Meissner, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In MICCAI 2019 International Workshop on Computational Diffusion MRI, 2019.  [bibtex] [arXiv:1806.02997] [pdf]Oral Presentation
[]Convex Optimisation for Inverse Kinematics (T. Yenamandra, F. Bernard, J. Wang, F. Mueller and C. Theobalt), In 2019 International Conference on 3D Vision (3DV), 2019.  [bibtex]Oral Presentation
[]Variational Uncalibrated Photometric Stereo under General Lighting (B. Haefner, Z. Ye, M. Gao, T. Wu, Y. Quéau and D. Cremers), In IEEE/CVF International Conference on Computer Vision (ICCV), 2019. ([supp] [poster] [matlab] [python] [cvf]) [bibtex] [doi] [arXiv:1904.03942] [pdf]
[]Photometric Segmentation: Simultaneous Photometric Stereo and Masking (B. Haefner, Y. Quéau and D. Cremers), In International Conference on 3D Vision (3DV), 2019. ([poster] [slides]) [bibtex] [doi] [pdf]Spotlight Presentation
[]Shape Correspondence with Isometric and Non-Isometric Deformations (R. Dyke, C. Stride, Y.-K. Lai, P. L. Rosin, M. Aubry, A. Boyarski, A. M. Bronstein, M. M. Bronstein, D. Cremers, M. Fisher, T. Groueix, D. Guo, V. G. Kim, R. Kimmel, Z. Lähner, K. Li, O. Litany, T. Remez, E. Rodolà, B. C. Russell, Y. Sahillioglu, R. Slossberg, G. K. L. Tam, M. Vestner, Z. Wu and J. Yang), In 12th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2019, Genoa, Italy, May 5-6, 2019 (S Biasotti, G Lavoué, RC. Veltkamp, eds.), Eurographics Association, 2019.  [bibtex]
2018
[]Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs (P. Wenzel, Q. Khan, D. Cremers and L. Leal-Taixé), In Conference on Robot Learning (CoRL), 2018. ([arXiv][videos][poster]) [bibtex] [pdf]
[]Simultaneous Calibration and Mapping (C. Nissler, M. Durner, Z.-C. Márton and R. Triebel), In International Symposium on Experimental Robotics (ISER), 2018.  [bibtex] [pdf]
[]6DoF Pose Estimation for Industrial Manipulation based on Synthetic Data (M. Brucker, M. Durner, Z.-C. Márton, F. Bálint-Benczédi, M. Sundermeyer and R. Triebel), In International Symposium on Experimental Robotics (ISER), 2018.  [bibtex] [pdf]
[]Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis (V. Estellers, F. Schmidt and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2018. ([Code]) [bibtex] [pdf]Received the Best Paper Award at 3DV 2018
[]Incremental Semi-Supervised Learning from Streams for Object Classification (I. Chiotellis, F. Zimmermann, D. Cremers and R. Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([code]) [bibtex] [pdf]
[]Appearance-Based Along-Route Localization for Planetary Missions (I. Grixa, P. Schulz, W. Stürzl and R. Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2018.  [bibtex] [pdf]
[]Persistent Anytime Learning of Objects from Unseen Classes (M. Denninger and R. Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2018.  [bibtex] [pdf]Best Cognitive Robotics Paper Finalist
[]Implicit 3D Orientation Learning for 6D Object Detection from RGB Images (M. Sundermeyer, Z. Marton, M. Durner, M. Brucker and R. Triebel), In European Conference on Computer Vision (ECCV), 2018.  [bibtex] [pdf]Best Paper Award
[]The Double Sphere Camera Model (V. Usenko, N. Demmel and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2018. ([arxiv]) [bibtex] [arXiv:1807.08957] [pdf]
[]Direct Sparse Odometry With Rolling Shutter (D. Schubert, N. Demmel, V. Usenko, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([supplementary][arxiv]) [bibtex] [pdf]Oral Presentation
[]Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (N. Yang, R. Wang, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([arxiv],[supplementary],[project]) [bibtex]Oral Presentation
[]DeepWrinkles: Accurate and Realistic Clothing Modeling (Z. Lähner, D. Cremers and T. Tung), In European Conference on Computer Vision (ECCV), 2018. ([Homepage], [Oral Presentation]) [bibtex] [pdf]Oral Presentation
[]LDSO: Direct Sparse Odometry with Loop Closure (X. Gao, R. Wang, N. Demmel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv][video][code][project]) [bibtex]
[]The TUM VI Benchmark for Evaluating Visual-Inertial Odometry (D. Schubert, T. Goll, N. Demmel, V. Usenko, J. Stueckler and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv]) [bibtex] [pdf]
[]Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization (L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2018. ([supplementary][video][arxiv]) [bibtex] [pdf]
[]Semantic Labeling of Indoor Environments from 3D RGB Maps (M. Brucker, M. Durner, R. Ambrus, Z.-C. Marton, A. Wendt, P. Jensfelt, K.O. Arras and R. Triebel), In International Conference on Robotics and Automation (ICRA), 2018.  [bibtex]
[]Proximal Backpropagation (T. Frerix, T. Möllenhoff, M. Moeller and D. Cremers), In International Conference on Learning Representations (ICLR), 2018. (arXiv:1706.04638, code) [bibtex]
[]Associative Deep Clustering - Training a Classification Network with no Labels (P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout and D. Cremers), In Proc. of the German Conference on Pattern Recognition (GCPR), 2018.  [bibtex] [pdf]
[]q-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization (V. Golkov, P. Swazinna, M. M. Schmitt, Q. A. Khan, C. M. W. Tax, M. Serahlazau, F. Pasa, F. Pfeiffer, G. J. Biessels, A. Leemans and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
[]q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis (V. Golkov, A. Vasilev, F. Pasa, I. Lipp, W. Boubaker, E. Sgarlata, F. Pfeiffer, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
[]StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments (R Scona, M Jaimez, YR. Petillot, M Fallon and D Cremers), In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018, IEEE, 2018.  [bibtex]
[]Combinatorial Preconditioners for Proximal Algorithms on Graphs (T. Möllenhoff, Z. Ye, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.  [bibtex] [pdf]
[]A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.  [bibtex] [pdf]
[]MRF Optimization with Separable Convex Prior on Partially Ordered Labels (C Domokos, FR. Schmidt and D Cremers), In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VIII (V Ferrari, M Hebert, C Sminchisescu, Y Weiss, eds.), Springer, volume 11212, 2018.  [bibtex]
[]Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.  [bibtex] [pdf]
[]Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading (B. Haefner, Y. Quéau, T. Möllenhoff and D. Cremers), In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ([supp] [poster] [slides] [code] [cvf] [video]) [bibtex] [doi] [pdf] [video]Spotlight Presentation
[]Deep Depth From Focus (C. Hazirbas, S. G. Soyer, M. C. Staab, L. Leal-Taixé and D. Cremers), In Asian Conference on Computer Vision (ACCV), 2018. ([arxiv], Deep Depth From Focus,[dataset]) [bibtex]
[]Joint Representation of Primitive and Non-primitive Objects for 3D Vision (C. Sommer and D. Cremers), In 2018 International Conference on 3D Vision, 3DV 2018, Verona, Italy, September 5-8, 2018, IEEE Computer Society, 2018.  [bibtex] [doi]
[]Fusion of Head and Full-Body Detectors for Multi-Object Tracking (R. Henschel, L. Leal-Taixé, D. Cremers and B. Rosenhahn), In 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2018, Salt Lake City, UT, USA, June 18-22, 2018, IEEE Computer Society, 2018.  [bibtex]
2017
[]SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction (F. Engelmann, J. Stueckler and B. Leibe), In IEEE Winter Conference on Applications of Computer Vision, WACV, 2017.  [bibtex]
[]Keyframe-Based Visual-Inertial Online SLAM with Relocalization (A. Kasyanov, F. Engelmann, J. Stueckler and B. Leibe), In IEEE/RSJ Int. Conference on Intelligent Robots and Systems, IROS, 2017.  [bibtex]
[]Semi-Supervised Deep Learning for Monocular Depth Map Prediction (Y. Kuznietsov, J. Stueckler and B. Leibe), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex]
[]A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching (F. Bernard, F. R. Schmidt, J. Thunberg and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]A Variational Approach to Shape-from-shading Under Natural Illumination (Y. Quéau, J. Mélou, F. Castan, D. Cremers and J.-D. Durou), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017. ([codes]) [bibtex] [doi] [pdf]
[]Experience-based Optimization of Robotic Perception (M Durner, S Kriegel, S Riedel, M Brucker, ZC Marton, F Balint-Benczedi and R Triebel), In International Conference on Advanced Robotics (ICAR), 2017.  [bibtex]
[]How Robots Learn to Classify New Objects Trained from Small Data Sets (TS Wang, ZC Marton, M Brucker and R Triebel), In Conference on Robot Learning (CoRL), 2017.  [bibtex]
[]A Method for Hand-Eye and Camera-to-Camera Calibration for Limited Fields of View (C Nissler, ZC Marton, H Kisner, U Thomas and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex]
[]Selecting CNN Features for Online Learning of 3D Objects (M Ullrich, H Ali, M Durner, ZC Marton and R Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex]
[]Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems (T. Möllenhoff and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supp]) [bibtex] [pdf]
[]Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (R. Wang, M. Schwörer and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supplementary][video][arxiv][project]) [bibtex] [pdf]
[]Depth Super-Resolution Meets Uncalibrated Photometric Stereo (S. Peng, B. Haefner, Y. Quéau and D. Cremers), In IEEE International Conference on Computer Vision Workshops (ICCVW), 2017. ([code][slides] [cvf]) [bibtex] [doi] [arXiv:1708.00411] [pdf]Oral Presentation at ICCV Workshop on Color and Photometry in Computer Vision
[]Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (R. Maier, K. Kim, D. Cremers, J. Kautz and M. Niessner), In International Conference on Computer Vision (ICCV), 2017. ([slides] [poster] [dataset] [code]) [bibtex] [pdf]
[]Multiframe Scene Flow with Piecewise Rigid Motion (V. Golyanik, K. Kim, R. Maier, M. Niessner, D. Stricker and J. Kautz), In International Conference on 3D Vision (3DV), 2017. ([slides] [poster] [supplementary]) [bibtex] [pdf]Spotlight Presentation
[]Multiframe Motion Coupling for Video Super Resolution (J. Geiping, H. Dirks and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition - 11th International Conference, EMMCVPR 2017, Venice, Italy, October 30 - November 1, 2017, Revised Selected Papers (M Pelillo, ER. Hancock, eds.), Springer, volume 10746, 2017.  [bibtex]
[]Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction (R. Maier, R. Schaller and D. Cremers), In British Machine Vision Conference (BMVC), 2017. ([poster] [supplementary]) [bibtex] [pdf]
[]Efficient Deformable Shape Correspondence via Kernel Matching (M. Vestner, Z. Lähner, A. Boyarski, O. Litany, R. Slossberg, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, R. Kimmel and D. Cremers), In International Conference on 3D Vision (3DV), 2017. ([arxiv],[Code]) [bibtex] [pdf]Oral Presentation
[]Stéréophotométrie microscopique sans démosaïquage (Y. Quéau, M. Pizenberg, D. Cremers and J.-D. Durou), In GRETSI, 2017.  [bibtex] [pdf]
[]Associative Domain Adaptation (P. Haeusser, T. Frerix, A. Mordvintsev and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Better Text Understanding Through Image-To-Text Transfer (K. Kurach, S. Gelly, M. Jastrzebski, P. Haeusser, O. Teytaud, D. Vincent and O. Bousquet), In arxiv:1705.08386, 2017.  [bibtex] [pdf]
[]Dense Multi-view 3D-reconstruction Without Dense Correspondences (Y. Quéau, J. Mélou, J.-D. Durou and D. Cremers), In ArXiv preprint 1704.00337, 2017. ([arxiv]) [bibtex] [pdf]
[]One-Shot Video Object Segmentation (S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers and L. V Gool), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (T. Meinhardt, M. Moeller, C. Hazirbas and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv], [code]) [bibtex]
[]A Non-Convex Variational Approach to Photometric Stereo under Inaccurate Lighting (Y. Quéau, T. Wu, F. Lauze, J.-D. Durou and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([codes]) [bibtex] [pdf]
[]Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data (F. Lauze, Y. Quéau and E. Plenge), In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017.  [bibtex] [pdf]
[]Beyond Multi-view Stereo: Shading-Reflectance Decomposition (J. Mélou, Y. Quéau, J.-D. Durou, F. Castan and D Cremers), In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017.  [bibtex] [pdf]
[]Semi-Calibrated Near-Light Photometric Stereo (Y. Quéau, T. Wu and D. Cremers), In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017. ([codes]) [bibtex] [pdf]
[]Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer (V. Usenko, L. von Stumberg, A. Pangercic and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017. ([arxiv]) [bibtex] [pdf]Best Paper Award - Finalist (link)
[]KillingFusion: Non-rigid 3D Reconstruction without Correspondences (M. Slavcheva, M. Baust, D. Cremers and S. Ilic), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]Learning by Association - A versatile semi-supervised training method for neural networks (P. Haeusser, A. Mordvintsev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing (Y. Quéau, M. Pizenberg, J.-D. Durou and D. Cremers), In International Conference on Quality Control by Artificial Vision (QCAV), 2017. ([codes]) [bibtex] [pdf]
[]Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients (J.C. Peeken, C. Knie, V. Golkov, K. Kessel, F. Pasa, Q. Khan, M. Seroglazov, J. Kukačka, T. Goldberg, L. Richter, J. Reeb, B. Rost, F. Pfeiffer, D. Cremers, F. Nüsslin and S.E. Combs), In 23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO), 2017.  [bibtex]
[]Image-based localization using LSTMs for structured feature correlation (F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv]) [bibtex]
[]From Monocular SLAM to Autonomous Drone Exploration (L. von Stumberg, V. Usenko, J. Engel, J. Stueckler and D. Cremers), In European Conference on Mobile Robots (ECMR), 2017. ([arXiv]) [bibtex] [pdf]
[]De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors (M. Dzitsiuk, J. Sturm, R. Maier, L. Ma and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2017. ([video]) [bibtex] [pdf]
[]Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space (M. Vestner, R. Litman, E. Rodola, A. Bronstein and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([Code], also check the related github repository) [bibtex] [pdf]
[]Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras (L. Ma, J. Stueckler, C. Kerl and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex] [pdf]
[]An Efficient Background Term for 3D Reconstruction and Tracking with Smooth Subdivision Surface Models (M. Jaimez, T. J. Cashman, A. Fitzgibbon, J. Gonzalez-Jimenez and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([video]) [bibtex] [pdf]
[]Fast Odometry and Scene Flow from RGB-D Cameras based on Geometric Clustering (M. Jaimez, C. Kerl, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2017. ([video]) [bibtex] [pdf]
[]Real-time variational stereo reconstruction with applications to large-scale dense SLAM (G. Kuschk, A. Bozic and D. Cremers), In IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017, IEEE, 2017.  [bibtex]
[]Map-based drone homing using shortcuts (D. Bender, W. Koch and D. Cremers), In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, Korea (South), November 16-18, 2017, IEEE, 2017.  [bibtex]
[]Nonlinear Spectral Image Fusion (M. Benning, M. Möller, R. Z. Nossek, M. Burger, D. Cremers and G. Gilboa), In Scale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings (F. Lauze, Y. Dong, A. Dahl, eds.), Springer, volume 10302, 2017.  [bibtex]
2016
[]Joint Object Pose Estimation and Shape Reconstruction in Urban Street Scenes Using 3D Shape Priors (F. Engelmann, J. Stueckler and B. Leibe), In Proc. of the German Conference on Pattern Recognition (GCPR), 2016.  [bibtex]
[]Scene Flow Propagation for Semantic Mapping and Object Discovery in Dynamic Street Scenes (D. Kochanov, A. Osep, J. Stueckler and B. Leibe), In IEEE/RSJ Int. Conference on Intelligent Robots and Systems, IROS, 2016.  [bibtex]
[]Unsupervised Learning of Shape-Motion Patterns for Objects in Urban Street Scenes (D. Klostermann, A. Osep, J. Stueckler and B. Leibe), In British Machine Vision Conference (BMVC), 2016.  [bibtex]
[]Scaling the world of monocular SLAM with INS-measurements for UAS navigation (D. Bender, F. Rouatbi, M. Schikora, D. Cremers and W. Koch), In 19th International Conference on Information Fusion, FUSION 2016, Heidelberg, Germany, July 5-8, 2016, IEEE, 2016.  [bibtex]
[]Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks (S. Sharifzadeh, I. Chiotellis, R. Triebel and D. Cremers), In , NIPS Workshops, 2016. ([arxiv]) [bibtex] [pdf]
[]A Convex Solution to Spatially-Regularized Correspondence Problems (T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016.  [bibtex] [pdf]
[]FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos and D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bibtex] [pdf]
[]Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding (I. Chiotellis, R. Triebel, T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([code]) [bibtex] [pdf]
[]A position free boresight calibration for INS-camera systems (D. Bender, D. Cremers and W. Koch), In 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016, Baden-Baden, Germany, September 19-21, 2016, IEEE, 2016.  [bibtex]
[]Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies (E. Laude, T. Möllenhoff, M. Moeller, J. Lellmann and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([supp] [code]) [bibtex] [pdf]
[]Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In arXiv:1607.02565, 2016.  [bibtex] [pdf]
[]A Photometrically Calibrated Benchmark For Monocular Visual Odometry (J. Engel, V. Usenko and D. Cremers), In arXiv:1607.02555, 2016.  [bibtex] [pdf]
[]CPA-SLAM: Consistent Plane-Model Alignment for Direct RGB-D SLAM (L. Ma, C. Kerl, J. Stueckler and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf]
[]Sublabel-Accurate Relaxation of Nonconvex Energies (T. Möllenhoff, E. Laude, M. Moeller, J. Lellmann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([supp] [code]) [bibtex] [pdf]Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016
[]SHREC’16: Partial Matching of Deformable Shapes (L. Cosmo, E. Rodola, M. M. Bronstein, A. Torsello, D. Cremers and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. (to appear) [bibtex] [pdf]
[] SHREC’16: Matching of Deformable Shapes with Topological Noise (Z. Lähner, E. Rodola, M. M. Bronstein, D. Cremers, O. Burghard, L. Cosmo, A. Dieckmann, R. Klein and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. ([Dataset]) [bibtex] [pdf] [pdf]
[]Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects (A. Narr, R. Triebel and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf]
[]Direct Visual-Inertial Odometry with Stereo Cameras (V. Usenko, J. Engel, J. Stueckler and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf] [video]
[] Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (Z. Lähner, E. Rodola, F. R. Schmidt, M. M. Bronstein and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([Code], [Homepage]) [bibtex] [pdf] [pdf]
[]Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images (V. Golkov, M. J. Skwark, A. Golkov, A. Dosovitskiy, T. Brox, J. Meiler and D. Cremers), In Annual Conference on Neural Information Processing Systems (NIPS), 2016. ([video]) [bibtex] [pdf]Oral Presentation (acceptance rate: under 2%)
[]Model-Free Novelty-Based Diffusion MRI (V. Golkov, T. Sprenger, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann and D. Cremers), In IEEE International Symposium on Biomedical Imaging (ISBI), 2016.  [bibtex] [pdf]
[]A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy and T. Brox), In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, 2016.  [bibtex]
[]Planar Odometry from a Radial Laser Scanner. A Range Flow-based Approach (M. Jaimez, J. G. Monroy and J. Gonzalez-Jimenez), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2016. ([video]) [bibtex] [pdf]
[]A game-theoretical approach for joint matching of multiple feature throughout unordered images (L. Cosmo, A. Albarelli, F. Bergamasco, A. Torsello, E. Rodolà and D. Cremers), In 23rd International Conference on Pattern Recognition, ICPR 2016, Cancún, Mexico, December 4-8, 2016, IEEE, 2016.  [bibtex]
2015
[]A Novel Framework for Nonlocal Vectorial Total Variation Based on $\ell^{p,q,r}$−norms (J. Duran, M. Moeller, C. Sbert and D. Cremers), In Proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer International Publishing, 2015.  [bibtex]
[]Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations (J. Stühmer, S. Nowozin, A. Fitzgibbon, R. Szeliski, T. Perry, S. Acharya, D. Cremers and J. Shotton), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video]) [bibtex] [pdf]
[]Video Segmentation with Just a Few Strokes (N. Nagaraja, F. R. Schmidt and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015.  [bibtex] [pdf]
[]CAPTCHA Recognition with Active Deep Learning (F. Stark, C. Hazirbas, R. Triebel and D. Cremers), In GCPR Workshop on New Challenges in Neural Computation, 2015. ([code]) [bibtex] [pdf]
[]Entropy Minimization for Convex Relaxation Approaches (M. Souiai, M. R. Oswald, Y. Kee, J. Kim, M. Pollefeys and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. (accepted) [bibtex] [pdf]
[]Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras (C. Kerl, J. Stueckler and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video][supplementary][datasets]) [bibtex] [pdf]
[]Point-wise Map Recovery and Refinement from Functional Correspondence (E. Rodola, M. Moeller and D. Cremers), In Proceedings Vision, Modeling and Visualization (VMV), 2015.  [bibtex] [pdf]Received the Best Paper Award
[]Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images (M. Jaimez, M. Souiai, J. Stueckler, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2015. ([video]) [bibtex] [pdf]
[]Reconstructing Street-Scenes in Real-Time From a Driving Car (V. Usenko, J. Engel, J. Stueckler and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2015.  [bibtex] [pdf]
[]Super-Resolution Keyframe Fusion for 3D Modeling with High-Quality Textures (R. Maier, J. Stueckler and D. Cremers), In International Conference on 3D Vision (3DV), 2015. ([slides] [poster]) [bibtex] [pdf]
[]Semi-supervised Online Learning for Efficient Classification of Objects in 3D Data Streams (Y. Tao, R. Triebel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf]
[]Large-Scale Direct SLAM for Omnidirectional Cameras (D. Caruso, J. Engel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf] [video]
[]Large-Scale Direct SLAM with Stereo Cameras (J. Engel, J. Stueckler and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf] [video]
[] Real-Time Object Detection, Localization and Verification for Fast Robotic Depalletizing (D. Holz, A. Topalidou-Kyniazopoulou, J. Stueckler and S. Behnke), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf]
[]SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (R. Triebel, K. Arras, R. Alami, L. Beyer, S. Breuers, R. Chatila, M. Chetouani, D. Cremers, V. Evers, M. Fiore, H. Hung, O. A. I Ramírez, M. Joosse, H. Khambhaita, T. Kucner, B. Leibe, A. J. Lilienthal, T. Linder, M. Lohse, M. Magnusson, B. Okal, L. Palmieri, U. Rafi, M. van Rooij and L. Zhang), In Proc. Field and Service Robotics (FSR), 2015.  [bibtex] [pdf]
[]FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]
[]q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, P. Sämann, J. I. Sperl, T. Sprenger, M. Czisch, M. I. Menzel, P. A. Gómez, A. Haase, T. Brox and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.  [bibtex] [pdf]
[]Free-Breathing, Self-Navigated RUFIS Lung Imaging with Motion Compensated Image Reconstruction (A. Menini, V. Golkov and F. Wiesinger), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2015.  [bibtex]
[]Robustness of Phase Sensitive Reconstruction in Diffusion Spectrum Imaging (M.I. Menzel, T. Sprenger, E.T. Tan, V. Golkov, C.J. Hardy, L. Marinelli and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2015.  [bibtex]
[]Using Diffusion and Structural MRI for the Automated Segmentation of Multiple Sclerosis Lesions (P.A. Gómez, T. Sprenger, A.A. López, J.I. Sperl, B. Fernandez, M. Molina-Romero, X. Liu, V. Golkov, M. Czisch, P. Saemann, M.I. Menzel and B.H. Menze), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2015.  [bibtex]
[]Active Online Confidence Boosting for Efficient Object Classification (D. Mund, R. Triebel and D. Cremers), In Proc. IEEE International Conference on Robotics and Automation (ICRA), 2015.  [bibtex] [pdf]
[]Adopting an Unconstrained Ray Model in Light-field Cameras for 3D Shape Reconstruction (F. Bergamasco, A. Albarelli, L. Cosmo, A. Torsello, E. Rodola and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.  [bibtex] [pdf]
[]Analysis of Surface Parametrizations for Modern Photometric Stereo Modeling (R. Mecca, E. Rodola and D. Cremers), In International Conference on Quality Control by Artificial Vision (QCAV), 2015.  [bibtex] [pdf]
[]A Fast Projection Method for Connectivity Constraints in Image Segmentation (J. Stühmer and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker, eds.), 2015.  [bibtex] [pdf]
[]A Primal-Dual Framework for Real-Time Dense RGB-D Scene Flow (M. Jaimez, M. Souiai, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2015. ([video]) [bibtex] [pdf]
[]Low Rank Priors for Color Image Regularization (T. Möllenhoff, E. Strekalovskiy, M. Möller and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015.  [bibtex] [pdf]
[]グラフマッチング学習を用いたRGB-D画像からの物体検出 [Graph matching gakushuu wo mochiita RGB-D gazou kara no buttai kenshutsu] - Learning graph matching for object detection from RGB-D images (A. Kanezaki, E. Rodola and T. Harada), In 第20回ロボティクスシンポジア - Robotics Symposia (RS), 2015.  [bibtex] [pdf]
[]Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]Oral Presentation
[]Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]
[]Learning Nonlinear Spectral Filters for Color Image Reconstruction (M. Moeller, J. Diebold, G. Gilboa and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015.  [bibtex] [pdf]
2014
[]Dense Elastic 3D Shape Matching (F. R. Schmidt, T. Windheuser, U. Schlickewei and D. Cremers), In Global Optimization Methods, Springer, volume 8293, 2014.  [bibtex] [pdf]
[] Mobile Teleoperation Interfaces with Adjustable Autonomy for Personal Service Robots (M. Schwarz, J. Stueckler and S. Behnke), In Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, Advances in Computational Mathematics, 2014.  [bibtex] [pdf] [doi]
[] Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner (D. Droeschel, J. Stueckler and S. Behnke), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2014.  [bibtex] [pdf] [doi]
[] Efficient deformable registration of multi-resolution surfel maps for object manipulation skill transfer (J. Stueckler and S. Behnke), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2014.  [bibtex] [pdf] [doi]
[] Combining the Strengths of Sparse Interest Point and Dense Image Registration for RGB-D Odometry (J. Stueckler, A. Gutt and S. Behnke), In Proc. of the Joint 45th International Symposium on Robotics (ISR) and 8th German Conference on Robotics (ROBOTIK), 2014.  [bibtex] [pdf]
[] Local Multi-Resolution Surfel Grids for MAV Motion Estimation and 3D Mapping (D. Droeschel, J. Stueckler and S. Behnke), In Proc. of the 13th International Conference on Intelligent Autonomous Systems (IAS), 2014.  [bibtex] [pdf]
[] Adaptive Tool-Use Strategies for Anthropomorphic Service Robots (J. Stueckler and S. Behnke), In Proc. of the 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014.  [bibtex] [pdf]
[]Towards Illumination-invariant 3D Reconstruction using ToF RGB-D Cameras (C. Kerl, M. Souiai, J. Sturm and D. Cremers), In International Conference on 3D Vision (3DV), 2014. ([supplementary]) [bibtex] [pdf]
[]INS-Camera Calibration without Ground Control Points (D. Bender, M. Schikora, J. Sturm and D. Cremers), In 9th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2014.  [bibtex] [pdf]
[]Learning Similarities for Rigid and Non-Rigid Object Detection (A. Kanezaki, E. Rodola, D. Cremers and T. Harada), In International Conference on 3D Vision (3DV), 2014.  [bibtex] [pdf]
[]Environment-adaptive Learning: How Clustering Helps to Obtain Good Training Data (S. Debnath, S. S. Baishya, R. Triebel, V. Dutt and D. Cremers), In KI 2014: Advances in Artificial Intelligence (C Lutz, M Thielscher, eds.), Springer, 2014.  [bibtex] [pdf]
[]Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes (R. Triebel, J. Stühmer, M. Souiai and D. Cremers), In German Conference on Pattern Recognition, 2014.  [bibtex] [pdf]
[]Visual-Inertial Navigation for a Camera-Equipped 25g Nano-Quadrotor (O. Dunkley, J. Engel, J. Sturm and D. Cremers), In IROS2014 Aerial Open Source Robotics Workshop, 2014.  [bibtex] [pdf] [video]
[]Anisotropic Laplace-Beltrami Operators for Shape Analysis (M. Andreux, E. Rodola, M. Aubry and D. Cremers), In Sixth Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA), 2014.  [bibtex] [pdf]
[]対応点集合類似度学習を用いた剛体・非剛体物体検出 [Taiou tenshuugou ruijido gakushuu wo mochiita goutai-higoutai buttai kenshutsu] (A. Kanezaki, E. Rodola, D. Cremers and T. Harada), In 信学技報 - Pattern Recognition and Media Understanding (PRMU), volume 114, 2014.  [bibtex] [pdf]
[]RGB-D画像からの物体検出における対応点集合類似度の学習 [RGB-D gazou kara no buttai kenshutsu ni okeru taiou tenshuugou ruijido no gakushuu] (A. Kanezaki, E. Rodola and T. Harada), In 第32回日本ロボット学会学術講演会 - The Robotics Society of Japan (RSJ), 2014.  [bibtex] [pdf]2015 研究奨励賞 Encouragement Award
[]Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional (E. Strekalovskiy and D. Cremers), In European Conference on Computer Vision (ECCV), 2014. (Code available) [bibtex] [pdf] [video]
[]Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction (M. R. Oswald, J. Stühmer and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf] [video]
[]Co-Sparse Textural Similarity for Interactive Segmentation (C. Nieuwenhuis, S. Hawe, M. Kleinsteuber and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf]
[]Surface Normal Integration for Convex Space-time Multi-view Reconstruction (M. R. Oswald and D. Cremers), In British Machine Vision Conference (BMVC), 2014.  [bibtex] [pdf] [video]
[]Spatial and Temporal Interpolation of Multi-View Image Sequences (T. Gurdan, M. R. Oswald, D. Gurdan and D. Cremers), In German Conference on Pattern Recognition (GCPR), volume 36, 2014.  [bibtex] [pdf] [video]
[]Submap-based Bundle Adjustment for 3D Reconstruction from RGB-D Data (R. Maier, J. Sturm and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014. ([slides]) [bibtex] [pdf]Oral Presentation
[]Flow and Color Inpainting for Video Completion (M. Strobel, J. Diebold and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014.  [bibtex] [doi] [pdf]Oral Presentation
[]Optimal Intrinsic Descriptors for Non-Rigid Shape Analysis (T. Windheuser, M. Vestner, E. Rodola, R. Triebel and D. Cremers), In British Machine Vision Conference (BMVC), 2014.  [bibtex] [pdf]
[]Semi-Dense Visual Odometry for AR on a Smartphone (T. Schöps, J. Engel and D. Cremers), In International Symposium on Mixed and Augmented Reality, 2014.  [bibtex] [pdf] [video]Best Short Paper Award
[]LSD-SLAM: Large-Scale Direct Monocular SLAM (J. Engel, T. Schöps and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf] [video]Oral Presentation
[]Collision Avoidance for Quadrotors with a Monocular Camera (H. Alvarez, L.M. Paz, J. Sturm and D. Cremers), In Proc. of The 12th International Symposium on Experimental Robotics (ISER), 2014.  [bibtex] [pdf]
[]Sequential Convex Relaxation for Mutual-Information-Based Unsupervised Figure-Ground Segmentation (Y. Kee, M. Souiai, D. Cremers and J. Kim), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.  [bibtex] [pdf]
[]Dense Non-Rigid Shape Correspondence Using Random Forests (E. Rodola, S. R Bulo, T. Windheuser, M. Vestner and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.  [bibtex] [pdf] [code]
[]Volumetric 3D Mapping in Real-Time on a CPU (F. Steinbruecker, J. Sturm and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2014.  [bibtex] [pdf]
[]Event-based 3D SLAM with a depth-augmented dynamic vision sensor (D. B. AD. CJ. C D. Weikersdorfer), In International Conference on Robotics and Automation (ICRA), 2014.  [bibtex] [pdf]
[]Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using 6-D Compressed Sensing (V. Golkov, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers and J.I. Sperl), In Organization for Human Brain Mapping (OHBM) Annual Meeting, 2014.  [bibtex]
[]Semi-Joint Reconstruction for Diffusion MRI Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images (V. Golkov, M.I. Menzel, T. Sprenger, A. Haase, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
[]Direct Reconstruction of the Average Diffusion Propagator with Simultaneous Compressed-Sensing-Accelerated Diffusion Spectrum Imaging and Image Denoising by Means of Total Generalized Variation Regularization (V. Golkov, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
[]Total Variation-Regularized Compressed Sensing Reconstruction for Multi-Shell Diffusion Kurtosis Imaging (J.I. Sperl, T. Sprenger, E.T. Tan, V. Golkov, M.I. Menzel, C.J. Hardy and L. Marinelli), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
[]Novel Acquisition Scheme for Diffusion Kurtosis Imaging Based on Compressed-Sensing Accelerated DSI Yielding Superior Image Quality (T. Sprenger, J.I. Sperl, B. Fernandez, V. Golkov, E.T. Tan, C.J. Hardy, L. Marinelli, M. Czisch, P. Sämann, A. Haase and M.I. Menzel), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
2013
[]Planar simplification and texturing of dense point cloud maps (L. Ma, T. Whelan, E. Bondarev, P. H. N. de With and J. McDonald), In Mobile Robots (ECMR), 2013 European Conference on, 2013.  [bibtex] [doi] [pdf] [video]
[]Fast Trust Region for Segmentation (L. Gorelick, F. R. Schmidt and Y. Boykov), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex] [pdf]
[] Mobile bin picking with an anthropomorphic service robot (M. Nieuwenhuisen, D. Droeschel, D. Holz, J. Stueckler, A. Berner, J Li, R. Klein and S. Behnke), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2013.  [bibtex] [pdf] [doi]
[] Hierarchical Object Discovery and Dense Modelling From Motion Cues in RGB-D Video (J. Stueckler and S. Behnke), In Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), IJCAI/AAAI, 2013.  [bibtex] [pdf]
[] Combining contour and shape primitives for object detection and pose estimation of prefabricated parts (A. Berner, J Li, D. Holz, J. Stueckler, S. Behnke and R. Klein), In Proc. of the 20th IEEE International Conference on Image Processing (ICIP), 2013.  [bibtex] [pdf] [doi]
[] Distinctive 3D surface entropy features for place recognition. (T. Fiolka, J. Stueckler, D. A. Klein, D. Schulz and S. Behnke), In Proc. of the European Conference on Mobile Robots (ECMR), IEEE, 2013.  [bibtex] [pdf]
[] Joint detection and pose tracking of multi-resolution surfel models in RGB-D (M. McElhone, J. Stueckler and S. Behnke), In Proc. of the European Conference on Mobile Robots (ECMR), IEEE, 2013.  [bibtex] [pdf]
[] Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video (J. Stueckler and S. Behnke), In Proc. of the British Machine Vision Conference (BMVC), 2013.  [bibtex] [pdf]
[] Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner (M. Schadler, J. Stueckler and S. Behnke), In Proc. of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2013.  [bibtex] [pdf] [doi]
[]Relaxations for Minimizing Metric Distortion and Elastic Energies for 3D Shape Matching (D. Cremers, E. Rodola and T. Windheuser), In Actes des recontres du CIRM: Courbure discrete: théorie et applications, volume 3, 2013.  [bibtex] [pdf]
[]Driven Learning for Driving: How Introspection Improves Semantic Mapping (R. Triebel, H. Grimmett, R. Paul and I. Posner), In The International Symposium on Robotics Research (ISRR), 2013.  [bibtex] [pdf]
[]Interactive Person Following and Gesture Recognition with a Flying Robot (T. Naseer, J. Sturm and D. Cremers), In Proc. of the Assistance and Service Robotics Workshop (ASROB) at the IEEE. Int. Conf. on Intelligent Robots and Systems (IROS), 2013.  [bibtex] [pdf]
[]Large-Scale Multi-Resolution Surface Reconstruction from RGB-D Sequences (F. Steinbruecker, C. Kerl, J. Sturm and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]
[]A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction (M. R. Oswald and D. Cremers), In ICCV Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2013.  [bibtex] [pdf]
[]Fast and Accurate Large-scale Stereo Reconstruction using Variational Methods (G. Kuschk and D. Cremers), In ICCV Workshop on Big Data in 3D Computer Vision, 2013.  [bibtex] [pdf]
[]Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs (J. Stühmer, P. Schröder and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]Oral Presentation
[]Proportion Priors for Image Sequence Segmentation (C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013. (oral presentation) [bibtex] [pdf] [video]
[]Total Variation Regularization for Functions with Values in a Manifold (J. Lellmann, E. Strekalovskiy, S. Koetter and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]
[]Elastic Net Constraints for Shape Matching (E. Rodola, A. Torsello, T. Harada, Y. Kuniyoshi and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf] [code]
[]Semi-Dense Visual Odometry for a Monocular Camera (J. Engel, J. Sturm and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf] [video]
[]Efficient Shape Matching using Vector Extrapolation (E. Rodola, T. Harada, Y. Kuniyoshi and D. Cremers), In British Machine Vision Conference (BMVC), 2013.  [bibtex] [pdf]
[]CopyMe3D: Scanning and Printing Persons in 3D (J. Sturm, E. Bylow, F. Kahl and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]Graph-based bundle adjustment for INS-camera calibration (D. Bender, M. Schikora, J. Sturm and D. Cremers), In Unmanned Aerial Vehicle in Geomatics (UAV-g), 2013.  [bibtex] [pdf]Best research paper award
[]Dense Tracking and Mapping with a Quadrocopter (J. Sturm, E. Bylow, F. Kahl and D. Cremers), In Unmanned Aerial Vehicle in Geomatics (UAV-g), 2013.  [bibtex] [pdf]
[]Scale-Aware Object Tracking with Convex Shape Constraints on RGB-D Images (M. Klodt, J. Sturm and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]FollowMe: Person Following and Gesture Recognition with a Quadrocopter (T. Naseer, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.  [bibtex] [pdf]
[]Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.  [bibtex] [pdf]
[]Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints (T. Möllenhoff, C. Nieuwenhuis, E. Toeppe and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2013.  [bibtex] [pdf]
[]Performance Evaluation of Narrow Band Methods for Variational Stereo (F. Stangl, M. Souiai and D. Cremers), In 35th German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]A Co-occurrence Prior for Continuous Multi-Label Optimization (M. Souiai, E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2013.  [bibtex] [pdf]
[]Learning Probabilistic Models for Mobile Manipulation Robots (J. Sturm and W. Burgard), In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), Track on Best papers in Sister Conferences, 2013.  [bibtex] [pdf]
[]Direct Camera Pose Tracking and Mapping With Signed Distance Functions (E. Bylow, J. Sturm, C. Kerl, F. Kahl and D. Cremers), In Demo Track of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at the Robotics: Science and Systems Conference (RSS), 2013.  [bibtex] [pdf]
[]Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions (E. Bylow, J. Sturm, C. Kerl, F. Kahl and D. Cremers), In Robotics: Science and Systems Conference (RSS), 2013.  [bibtex] [pdf]
[]Introspective Active Learning for Scalable Semantic Mapping (R. Triebel, H. Grimmett, R. Paul and I. Posner), In Workshop on Active Learning in Robotics: Exploration, Curiosity, and Interaction at Robotics: Science and Systems, 2013.  [bibtex] [pdf]
[]Confidence Boosting: Improving the Introspectiveness of a Boosted Classifier for Efficient Learning (R. Triebel, H. Grimmett and I. Posner), In Autonomous Learning Workshop at ICRA, 2013.  [bibtex] [pdf]
[]Depth-adative Supervoxels for RGB-D Video Segmentation (A. SD. C D. Weikersdorfer), In Proceedings of the IEEE International Conference on Image Processing, 2013.  [bibtex] [pdf]
[]Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making (H. Grimmett, R. Paul, R. Triebel and I. Posner), In IEEE International Conference on Robotics and Automation (ICRA), 2013.  [bibtex] [pdf]
[]Toward Automated Driving in Cities using Close-to-Market Sensors (P. Furgale, U. Schwesinger, M. Rufli, W. Derendarz, H. Grimmett, P. Mühlfellner, S. Wonneberger, J. Timpner, S. Rottmann, B. Li, B. Schmidt, T. N Nguyen, E. Cardarelli, S. Cattani, S. Brüning, S. Horstmann, M. Stellmacher, H. Mielenz, K. Köser, M. Beermann, C. Häne, L. Heng, G. H. Lee, F. Fraundorfer, R. Iser, R. Triebel, I. Posner, P. Newman, L. Wolf, M. Pollefeys, S. Brosig, J. Effertz, C. Pradalier and R. Siegwart), In Proc. of IEEE Intelligent Vehicles Symposium, 2013.  [bibtex] [pdf]
[]Volume Constraints for Single View Reconstruction (E. Toeppe, C. Nieuwenhuis and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex]
[]Robust Odometry Estimation for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2013.  [bibtex] [pdf]Best Vision Paper Award - Finalist
[]Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of SENSE Reconstruction into Joint Reconstruction in Combination with q-Space Compressed Sensing (V. Golkov, T. Sprenger, M.I. Menzel, E.T. Tan, K.F. King, C.J. Hardy, L. Marinelli, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2013.  [bibtex]
[]Phase Sensitive Reconstruction in Diffusion Spectrum Imaging Enabling Velocity Encoding and Unbiased Noise Distribution (J.I. Sperl, E.T. Tan, T. Sprenger, V. Golkov, K.F. King, C.J. Hardy, L. Marinelli and M.I. Menzel), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2013.  [bibtex]
[]SNR-dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom (T. Sprenger, B. Fernandez, J.I. Sperl, V. Golkov, M. Bach, E.T. Tan, K.F. King, C.J. Hardy, L. Marinelli, M. Czisch, P. Sämann, A. Haase and M.I. Menzel), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2013.  [bibtex]
[]Corrected Joint SENSE Reconstruction, Low-Rank Constraints, and Compressed-Sensing-Accelerated Diffusion Spectrum Imaging in Denoising and Kurtosis Tensor Estimation (V. Golkov, M.I. Menzel, T. Sprenger, A. Menini, D. Cremers and J.I. Sperl), In ISMRM Workshop on Diffusion as a Probe of Neural Tissue Microstructure, 2013.  [bibtex]
[]Reconstruction, Regularization, and Quality in Diffusion MRI Using the Example of Accelerated Diffusion Spectrum Imaging (V. Golkov, M.I. Menzel, T. Sprenger, A. Menini, D. Cremers and J.I. Sperl), In 16th Annual Meeting of the German Chapter of the ISMRM, 2013.  [bibtex]Oral Presentation
[]Line-Process-Based Joint SENSE Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction (V. Golkov, T. Sprenger, M.I. Menzel, D. Cremers and J.I. Sperl), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2013.  [bibtex]Certificate of Merit Award
[]Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation (V. Golkov, T. Sprenger, A. Menini, M.I. Menzel, D. Cremers and J.I. Sperl), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2013.  [bibtex]Oral Presentation
[]Proximity Priors for Variational Semantic Segmentation and Recognition (J. Bergbauer, C. Nieuwenhuis, M. Souiai and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [doi] [pdf]
[]Convex Optimization for Scene Understanding (M. Souiai, C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [pdf]
[]Can a fully unconstrained imaging model be applied effectively to central cameras? (F. Bergamasco, A. Albarelli, E. Rodola and A. Torsello), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex] [pdf]
[]Top-down visual search in Wimmelbild (J Bergbauer and S Tari), In Proceedings of SPIE, Human Vision and Electronic Imaging XVIII, 2013.  [bibtex] [doi] [pdf]
[]Wimmelbild Analysis with Approximate Curvature Coding Distance Images (J Bergbauer and S Tari), In Scale Space and Variational Methods in Computer Vision (A. Kuijper, K. Bredies, T. Pock, H. Bischof, eds.), Springer, volume 7893, 2013.  [bibtex] [doi] [pdf]Oral Presentation
2012
[]Segmentation with non-linear regional constraints via line-search cuts (L. Gorelick, F. R. Schmidt, Y. Boykov, A. Delong and A. Ward), In European Conference on Computer Vision (ECCV), Springer, volume 7572, 2012.  [bibtex] [pdf]
[]Hausdorff Distance Constraint for Multi-Surface Segmentation (F. R. Schmidt and Y. Boykov), In European Conference on Computer Vision (ECCV), Springer, volume 7572, 2012.  [bibtex] [pdf]
[]Furniture Classification using WWW CAD Models (V. Usenko, F. Seidel, Z. Marton, D. Pangercic and M. Beetz), In IROS'12 Workshop on Active Semantic Perception, 2012.  [bibtex] [pdf]
[] Robust Real-Time Registration of RGB-D Images using Multi-Resolution Surfel Representations (J. Stueckler and S. Behnke), In Proc. of ROBOTIK, VDE-Verlag, 2012.  [bibtex] [pdf]
[] Efficient Mobile Robot Navigation using 3D Surfel Grid Maps (J. Kläß, J. Stueckler and S. Behnke), In Proc. of ROBOTIK, VDE-Verlag, 2012.  [bibtex] [pdf]
[] Shape-Primitive Based Object Recognition and Grasping (M. Nieuwenhuisen, J. Stueckler, A. Berner, R. Klein and S. Behnke), In Proc. of ROBOTIK, VDE-Verlag, 2012.  [bibtex] [pdf]
[] Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps (J. Stueckler and S. Behnke), In , 2012.  [bibtex] [pdf]
[]Adaptive Multi-cue 3D Tracking of Arbitrary Objects (G. M. García, D. A. Klein, J. Stueckler, S. Frintrop and A. B. Cremers), In DAGM/OAGM Symposium (A Pinz, T Pock, H Bischof, F Leberl, eds.), Springer, volume 7476, 2012.  [bibtex]
[] SURE: Surface Entropy for Distinctive 3D Features (T. Fiolka, J. Stueckler, D. A. Klein, D. Schulz and S. Behnke), In Proc. of Spatial Cognition, 2012.  [bibtex] [pdf]
[] Adjustable autonomy for mobile teleoperation of personal service robots (S. Muszynski, J. Stueckler and S. Behnke), In Proc. of the IEEE Int. Symp. on Robot and Human Interactive Communication, 2012.  [bibtex] [pdf] [doi]
[] Integrating depth and color cues for dense multi-resolution scene mapping using RGB-D cameras (J. Stueckler and S. Behnke), In Proc. of the IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012.  [bibtex] [pdf] [doi]
[] Semantic mapping using object-class segmentation of RGB-D images (J. Stueckler, N. Biresev and S. Behnke), In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2012.  [bibtex] [pdf] [doi]
[] Bayesian calibration of the hand-eye kinematics of an anthropomorphic robot (U. Hubert, J. Stueckler and S. Behnke), In Proc. of the 12th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), 2012.  [bibtex] [pdf] [doi]
[]Parsing Outdoor Scenes from Streamed 3D Laser Data Using Online Clustering and Incremental Belief Updates (R. Triebel, R. Paul, D. Rus and P. Newman), In Robotics Track of AAAI Conference on Artificial Intelligence, 2012.  [bibtex] [pdf]
[]Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification (R. Paul, R. Triebel, D. Rus and P. Newman), In Proc. of the International Conference on Intelligent Robots and Systems (IROS), 2012.  [bibtex] [pdf]
[]Wehrli 2.0: An Algorithm for ”Tidying up Art” (N. Ufer, M. Souiai and D. Cremers), In VISART “Where Computer Vision Meets Art” workshop, ECCV 2012, Springer, 2012.  [bibtex] [pdf]
[]Comparison of Diffusion Kurtosis Tensor Estimation Methods in an Advanced Quality Assessment Framework (V. Golkov, J.I. Sperl, T. Sprenger, H.-J. Bungartz, M. Sedlacek, E.T. Tan, L. Marinelli, C.J. Hardy, K.F. King and M.I. Menzel), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2012.  [bibtex]
[]Evaluation of DSI Imaging with Compressed Sensing under the Presence of Different Noise Levels on a Diffusion Phantom (T. Sprenger, B. Fernandez, M. Bach, J.I. Sperl, V. Golkov, E.T. Tan, L. Marinelli, K.F. King, C.J. Hardy, Q. Zhu, M. Czisch, P. Sämann, A. Haase and M.I. Menzel), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2012.  [bibtex]
[]Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark (J. Sturm, W. Burgard and D. Cremers), In Proc. of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Accurate Figure Flying with a Quadrocopter Using Onboard Visual and Inertial Sensing (J. Engel, J. Sturm and D. Cremers), In Proc. of the Workshop on Visual Control of Mobile Robots (ViCoMoR) at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]A Benchmark for the Evaluation of RGB-D SLAM Systems (J. Sturm, N. Engelhard, F. Endres, W. Burgard and D. Cremers), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Camera-Based Navigation of a Low-Cost Quadrocopter (J. Engel, J. Sturm and D. Cremers), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf] [video]
[]A Convex Representation for the Vectorial Mumford-Shah Functional (E. Strekalovskiy, A. Chambolle and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [pdf]
[]Fast and Globally Optimal Single View Reconstruction of Curved Objects (M. R. Oswald, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [pdf]
[]QPBOアルゴリズムの多値化による非劣モジュラエネルギー最小化 [QPBO arugorizumu no tachika ni yoru hiretsu mojura enerugī saishōka] (T. Windheuser, H. Ishikawa and D. Cremers), In Meeting on Image Recognition and Understanding, 2012.  [bibtex] [pdf]
[]Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency (T. Windheuser, H. Ishikawa and D. Cremers), In European Conference on Computer Vision (ECCV), 2012.  [bibtex] [pdf]
[]Nonmetric Priors for Continuous Multilabel Optimization (E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), In European Conference on Computer Vision (ECCV), Springer, 2012.  [bibtex] [pdf]
[]Real-Time Human Motion Tracking using Multiple Depth Cameras (L. Zhang, J. Sturm, D. Cremers and D. Lee), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Box-Particle PHD Filter for Multi-Target Tracking (M. Schikora, A. Gning, L. Mihaylova, D. Cremers and W. Koch), In 15th International Conference on Information Fusion (FUSION), 2012.  [bibtex] [pdf]
[]Box-Particle Intensity Filter (M. Schikora, A. Gning, L. Mihaylova, D. Cremers, W. Koch and R. Streit), In 9th IET Data Fusion and Target Tracking Conference, 2012.  [bibtex] [pdf]
[]Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction (D Joho, GD Tipaldi, N Engelhard, C Stachniss and W Burgard), In Proceedings of Robotics: Science and Systems, 2012.  [bibtex] [pdf]
[]A Generalized Framework for Opening Doors and Drawers in Kitchen Environments (T. Ruehr, J. Sturm, D. Pangercic, M. Beetz and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2012.  [bibtex] [pdf]
[]An Evaluation of the RGB-D SLAM System (F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers and W. Burgard), In International Conference on Robotics and Automation (ICRA), 2012.  [bibtex] [pdf]
[]A game-theoretic approach to deformable shape matching (E. Rodola, A.M. Bronstein, A. Albarelli, F. Bergamasco and A. Torsello), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [doi] [pdf]
2011
[]Interactive Segmentation with Super-Labels (A. Delong, L. Gorelick, F. R. Schmidt, O. Veksler and Y. Boykov), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 6819, 2011.  [bibtex] [pdf]
[]Multilinear Model Estimation with L2-Regularization (F. R. Schmidt, H. Ackermann and B. Rosenhahn), In Pattern Recognition (Proc. DAGM), Springer, volume 6835, 2011.  [bibtex] [pdf]
[] Learning to Interpret Pointing Gestures with a Time-of-flight Camera (D. Droeschel, J. Stueckler and S. Behnke), In Proceedings of the 6th International Conference on Human-robot Interaction, Advances in Computational Mathematics, 2011.  [bibtex] [pdf] [doi]
[] Interest point detection in depth images through scale-space surface analysis (J. Stueckler and S. Behnke), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2011.  [bibtex] [pdf] [doi]
[] Towards joint attention for a domestic service robot - person awareness and gesture recognition using Time-of-Flight cameras (D. Droeschel, J. Stueckler, D. Holz and S. Behnke), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2011.  [bibtex] [pdf] [doi]
[] Compliant Task-Space Control with Back-Drivable Servo Actuators (J. Stueckler and S. Behnke), In RoboCup (T Röfer, NM Mayer, J Savage, U Saranli, eds.), Springer, volume 7416, 2011.  [bibtex] [pdf]
[] Real-Time 3D Perception and Efficient Grasp Planning for Everyday Manipulation Tasks. (J. Stueckler, R. Steffens, D. Holz and S. Behnke), In Proc. of the European Conf. on Mobile Robots (ECMR), 2011.  [bibtex] [pdf]
[] Following human guidance to cooperatively carry a large object (J. Stueckler and S. Behnke), In Proc. of the 11th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), 2011.  [bibtex] [pdf] [doi]
[] Efficient Multi-resolution Plane Segmentation of 3D Point Clouds (B. Oehler, J. Stueckler, J. Welle, D. Schulz and S. Behnke), In Proc. of the Int. Conf. on Intelligent Robotics and Applications (ICIRA) (S Jeschke, H Liu, D Schilberg, eds.), Springer Berlin Heidelberg, volume 7102, 2011.  [bibtex] [pdf] [doi]
[]Bayesian On-line Learning of Driving Behaviors (J. Maye, R. Triebel, L. Spinello and R. Siegwart), In International Conference on Robotics and Automation (ICRA), 2011.  [bibtex] [pdf]
[]Unsupervised 3D Object Discovery and Categorization for Mobile Robots (J. Shin, R. Triebel and R. Siegwart), In Proc. of the International Symposium on Robotics Research (ISRR), 2011.  [bibtex] [pdf]
[]A Survey on Geometry Recovery from a Single Image with Focus on Curved Object Reconstruction (M. R. Oswald, E. Toeppe, C. Nieuwenhuis and D. Cremers), In Proceedings of the 2011 Conference on Innovations for Shape Analysis: Models and Algorithms, Springer-Verlag, 2011.  [bibtex] [pdf]
[]Silhouette-Based Variational Methods for Single View Reconstruction (E. Toeppe, M. R. Oswald, D. Cremers and C. Rother), In Proceedings of the 2010 international conference on Video Processing and Computational Video (D. Cremers, M. A. Magnor, M. R. Oswald, L. Zelnik-Manor, eds.), Springer-Verlag, 2011.  [bibtex] [pdf]
[]Multi-object tracking via high accuracy optical flow and finite set statistics (M. Schikora, W. Koch and D. Cremers), In International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011.  [bibtex] [pdf]
[]Multiple Emitter Localization Using a Realistic Airborne Array Sensor (M. Oispuu and M. Schikora), In 14th International Conference on Information Fusion (FUSION), 2011.  [bibtex] [pdf]
[]Sequential Monte Carlo Method for the iFilter (M. Schikora, W. Koch, R.L. Streit and D. Cremers), In 14th International Conference on Information Fusion (FUSION), 2011.  [bibtex] [pdf]
[]Pixel-based Classification Method for Detecting Unhealthy Regions in Leaf Images (S. Madhogaria, M. Schikora, W. Koch and D. Cremers), In 6th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2011.  [bibtex] [pdf]
[]Multiple Source Localization Based on Biased Bearings Using the Intensity Filter - Approach and Experimental Results (M. Schikora, M.Oispuu, W. Koch and D. Cremers), In 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2011.  [bibtex] [pdf]
[]Mobile Manipulation of Kitchen Containers (J. Becker, C. Bersch, D. Pangercic, B. Pitzer, T. Rühr, B. Sankaran, J. Sturm, C. Stachniss, M. Beetz and W. Burgard), In Proc. of the IROS'11 Workshop on Results, Challenges and Lessons Learned in Advancing Robots with a Common Platform, 2011.  [bibtex] [pdf]
[]Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm and D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]The Wave Kernel Signature: A Quantum Mechanical Approach To Shape Analysis (M. Aubry, U. Schlickewei and D. Cremers), In IEEE International Conference on Computer Vision (ICCV) - Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2011.  [bibtex] [pdf] [code]
[]A Convex Framework for Image Segmentation with Moment Constraints (M. Klodt and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches (C. Nieuwenhuis, E. Toeppe and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2011.  [bibtex] [pdf]
[]Towards a benchmark for RGB-D SLAM evaluation (J. Sturm, S. Magnenat, N. Engelhard, F. Pomerleau, F. Colas, W. Burgard, D. Cremers and R. Siegwart), In Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf. (RSS), 2011.  [bibtex] [pdf] [pdf]
[]Real-time 3D visual SLAM with a hand-held camera (N. Engelhard, F. Endres, J. Hess, J. Sturm and W. Burgard), In Proc. of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, 2011.  [bibtex] [pdf]
[]Learning the State Transition Model to Efficiently Clean Surfaces with Mobile Manipulation Robots (J. Hess, J. Sturm and W. Burgard), In Proc. of the Workshop on Manipulation under Uncertainty at the IEEE Int. Conf. on Robotics and Automation (ICRA), 2011.  [bibtex] [pdf]
[]Generalized Ordering Constraints for Multilabel Optimization (E. Strekalovskiy and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011. (oral presentation) [bibtex] [pdf]
[]Decoupling Photometry and Geometry in Dense Variational Camera Calibration (M. Aubry, K. Kolev, B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Tight Convex Relaxations for Vector-Valued Labeling Problems (E. Strekalovskiy, B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Introducing Total Curvature for Image Processing (B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Total Variation for Cyclic Structures: Convex Relaxation and Efficient Minimization (E. Strekalovskiy and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.  [bibtex] [pdf]
[]On a linear programming approach to the discrete Willmore boundary value problem and generalizations (T. Schoenemann, S. Masnou and D. Cremers), In Curves and Surfaces 2011 (J.-D. Bet al., ed.), 2011.  [bibtex] [pdf]
[]Pose-Consistent 3D Shape Segmentation Based on a Quantum Mechanical Feature Descriptor (M. Aubry, U. Schlickewei and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, 2011.