Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
spezial:bib [2020/03/24 12:08] Prof. Dr. Daniel Cremers |
spezial:bib [2024/04/13 11:37] Christoph Reich |
||
---|---|---|---|
Line 1: | Line 1: | ||
- | e@STRING{aap = {Archiv f{\" | + | @STRING{aap = {Archiv f{\" |
@STRING{accv = {Asian Conference on Computer Vision}} | @STRING{accv = {Asian Conference on Computer Vision}} | ||
Line 61: | Line 61: | ||
@STRING{comp = {Computing}} | @STRING{comp = {Computing}} | ||
+ | |||
+ | @STRING{corl = {Conference on Robot Learning (CoRL)}} | ||
@STRING{cpam = {Communications on Pure and Applied Mathematics}} | @STRING{cpam = {Communications on Pure and Applied Mathematics}} | ||
Line 83: | Line 85: | ||
@STRING{ecj = {Electronics and Communications in Japan}} | @STRING{ecj = {Electronics and Communications in Japan}} | ||
+ | |||
+ | @STRING{ecml = {European Conference on Machine Learning and Data Mining (ECML-PKDD)}} | ||
@STRING{ejde = {Proc. Energy Minimization Methods in Computer Vision and Pattern | @STRING{ejde = {Proc. Energy Minimization Methods in Computer Vision and Pattern | ||
Line 89: | Line 93: | ||
@STRING{emmcvpr = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}} | @STRING{emmcvpr = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}} | ||
- | @STRING{esmrmb = {European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting}} | + | @STRING{esmrmb = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}} |
@STRING{fcm = {Foundations of Computational Mathematics}} | @STRING{fcm = {Foundations of Computational Mathematics}} | ||
Line 106: | Line 110: | ||
@STRING{icip = {{P}roceedings of the {IEEE} {I}nternational {C}onference on {I}mage {P}rocessing}} | @STRING{icip = {{P}roceedings of the {IEEE} {I}nternational {C}onference on {I}mage {P}rocessing}} | ||
+ | |||
+ | @STRING{icml = {{I}nternational {C}onference on {M}achine {L}earning (ICML)}} | ||
@STRING{icra = {International Conference on Robotics and Automation (ICRA)}} | @STRING{icra = {International Conference on Robotics and Automation (ICRA)}} | ||
Line 127: | Line 133: | ||
@STRING{iros = {International Conference on Intelligent Robots and Systems (IROS)}} | @STRING{iros = {International Conference on Intelligent Robots and Systems (IROS)}} | ||
- | @STRING{isbi = {IEEE International Symposium on Biomedical Imaging (ISBI)}} | + | @STRING{isbi = {{IEEE} International Symposium on Biomedical Imaging ({ISBI})}} |
- | @STRING{ismrm = {International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting}} | + | @STRING{ismrm = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}} |
@STRING{ivc = {Image and Vision Computing}} | @STRING{ivc = {Image and Vision Computing}} | ||
Line 364: | Line 370: | ||
@STRING{vmv = {Proceedings Vision, Modeling and Visualization (VMV)}} | @STRING{vmv = {Proceedings Vision, Modeling and Visualization (VMV)}} | ||
+ | |||
+ | @STRING{wacv= {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}} | ||
@STRING{BMVC = {{B}ritish {M}achine {V}ision {C}onference (BMVC)}} | @STRING{BMVC = {{B}ritish {M}achine {V}ision {C}onference (BMVC)}} | ||
Line 370: | Line 378: | ||
@string{3dor = {Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR)}} | @string{3dor = {Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR)}} | ||
+ | |||
+ | @string{nipsd = {Neural Information Processing Systems Conference - NeurIPS 2021: eXplainable AI approaches for debugging and diagnosis workshop}} | ||
Line 384: | Line 394: | ||
doi = {10.1007/ | doi = {10.1007/ | ||
publisher = {Springer US}, | publisher = {Springer US}, | ||
- | | + | |
} | } | ||
Line 397: | Line 407: | ||
doi = {10.1016/ | doi = {10.1016/ | ||
publisher = {Elsevier}, | publisher = {Elsevier}, | ||
- | | + | |
} | } | ||
Line 412: | Line 422: | ||
issn = {0262-8856}, | issn = {0262-8856}, | ||
doi = {10.1016/ | doi = {10.1016/ | ||
- | | + | |
+ | } | ||
+ | |||
+ | @article{KuschkdGRC17, | ||
+ | author | ||
+ | P. d' | ||
+ | D. Gaudrie and | ||
+ | P. Reinartz and | ||
+ | D. Cremers}, | ||
+ | title = {Spatially Regularized Fusion of Multiresolution Digital Surface Models}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1477--1488}, | ||
+ | year = {2017}, | ||
+ | } | ||
+ | |||
+ | @article{CremersLV17, | ||
+ | author | ||
+ | L. Leal{-}Taix{\' | ||
+ | R. Vidal}, | ||
+ | title = {Deep Learning for Computer Vision (Dagstuhl Seminar 17391)}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {109--125}, | ||
+ | year = {2017}, | ||
+ | } | ||
+ | |||
+ | @incollection{Cremers15, | ||
+ | author | ||
+ | editor | ||
+ | title = {Image Segmentation with Shape Priors: Explicit Versus Implicit Representations}, | ||
+ | booktitle = {Handbook of Mathematical Methods in Imaging}, | ||
+ | pages = {1909--1944}, | ||
+ | publisher = {Springer}, | ||
+ | year = {2015}, | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{CosmoABTRC16, | ||
+ | author | ||
+ | A. Albarelli and | ||
+ | F. Bergamasco and | ||
+ | A. Torsello and | ||
+ | E. Rodol{\`{a}} and | ||
+ | D. Cremers}, | ||
+ | title = {A game-theoretical approach for joint matching of multiple feature | ||
+ | | ||
+ | booktitle = {23rd International Conference on Pattern Recognition, | ||
+ | | ||
+ | pages = {3715--3720}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2016}, | ||
} | } | ||
Line 429: | Line 492: | ||
publisher = {Springer US}, | publisher = {Springer US}, | ||
topic = {3D Reconstruction, | topic = {3D Reconstruction, | ||
- | } | + | }t |
@inproceedings{moeller-et-al-iccv15, | @inproceedings{moeller-et-al-iccv15, | ||
Line 437: | Line 500: | ||
year = {2015}, | year = {2015}, | ||
keywords = diebold, | keywords = diebold, | ||
+ | } | ||
+ | |||
+ | @inproceedings{Benning0N0CGS17, | ||
+ | author | ||
+ | M. M{\" | ||
+ | R. Z. Nossek and | ||
+ | M. Burger and | ||
+ | D. Cremers and | ||
+ | G. Gilboa and | ||
+ | | ||
+ | editor | ||
+ | Y. Dong and | ||
+ | A. Dahl}, | ||
+ | title = {Nonlinear Spectral Image Fusion}, | ||
+ | booktitle = {Scale Space and Variational Methods in Computer Vision - 6th International | ||
+ | | ||
+ | series | ||
+ | volume | ||
+ | pages = {41--53}, | ||
+ | publisher = {Springer}, | ||
+ | year = {2017}, | ||
} | } | ||
Line 448: | Line 532: | ||
doi = {10.1007/ | doi = {10.1007/ | ||
note = {{<a href=" | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{Toker_2022_CVPR, | ||
+ | author | ||
+ | and Andres, Camero and Hu, Jingliang and Hoderlein, Ariadna and Senaras, Caglar and Davis, Timothy and Cremers, Daniel and Marchisio, Giovanni and Zhu, Xiaoxiang and Leal-Taixe, Laura}, | ||
+ | title = {DynamicEarthNet: | ||
+ | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
+ | month = {June}, | ||
+ | titleurl = {toker-et-al-cvpr22.pdf}, | ||
+ | year = {2022} | ||
} | } | ||
Line 468: | Line 562: | ||
journal = ijcv, | journal = ijcv, | ||
issuetitle = {{Special Issue on Graphical Models for Scene Understanding}}, | issuetitle = {{Special Issue on Graphical Models for Scene Understanding}}, | ||
- | year = {2015}, | + | |
+ | number | ||
+ | pages = {199--225}, | ||
+ | | ||
publisher = {Springer US}, | publisher = {Springer US}, | ||
issn = {0920-5691}, | issn = {0920-5691}, | ||
Line 557: | Line 654: | ||
year = {2012}, | year = {2012}, | ||
titleurl = {rodola-cvpr12.pdf}, | titleurl = {rodola-cvpr12.pdf}, | ||
- | | + | |
} | } | ||
Line 593: | Line 690: | ||
doi={10.1109/ | doi={10.1109/ | ||
titleurl = {rodola-3dimpvt11-2.pdf}, | titleurl = {rodola-3dimpvt11-2.pdf}, | ||
- | | + | |
} | } | ||
Line 604: | Line 701: | ||
doi={10.1109/ | doi={10.1109/ | ||
titleurl = {rodola-3dimpvt11-1.pdf}, | titleurl = {rodola-3dimpvt11-1.pdf}, | ||
- | | + | |
} | } | ||
Line 663: | Line 760: | ||
year = {2010}, | year = {2010}, | ||
titleurl = {rodola-cvpr10.pdf}, | titleurl = {rodola-cvpr10.pdf}, | ||
- | | + | |
} | } | ||
Line 745: | Line 842: | ||
titleurl = {2010_sskkc_sdf.pdf}, | titleurl = {2010_sskkc_sdf.pdf}, | ||
keywords={biology} | keywords={biology} | ||
+ | } | ||
+ | |||
+ | @inproceedings{Bender0C17, | ||
+ | author | ||
+ | W. Koch and | ||
+ | D. Cremers}, | ||
+ | title = {Map-based drone homing using shortcuts}, | ||
+ | booktitle = {2017 {IEEE} International Conference on Multisensor Fusion and Integration | ||
+ | for Intelligent Systems, {MFI} 2017, Daegu, Korea (South), November | ||
+ | | ||
+ | pages = {505--511}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2017}, | ||
} | } | ||
Line 835: | Line 945: | ||
school = {Department of Computer Science, University of Bonn, Germany}, | school = {Department of Computer Science, University of Bonn, Germany}, | ||
year = {2008}, | year = {2008}, | ||
- | titleurl = {schoenemann_dissertation.pdf}, | + | titleurl = {schoenemann_dissertation.pdf}, |
} | } | ||
Line 863: | Line 973: | ||
month = | month = | ||
titleurl = {stuehmer_et_al_dagm10.pdf}, | titleurl = {stuehmer_et_al_dagm10.pdf}, | ||
- | keywords = {3d-reconstruction, | + | keywords = {3d-reconstruction, |
topic = {3D Reconstruction} | topic = {3D Reconstruction} | ||
} | } | ||
Line 874: | Line 984: | ||
address = | address = | ||
month = | month = | ||
- | keywords = {3d-reconstruction, | + | keywords = {3d-reconstruction, |
topic = {3D Reconstruction} | topic = {3D Reconstruction} | ||
} | } | ||
Line 1183: | Line 1293: | ||
@InProceedings{Pock2007_Registration, | @InProceedings{Pock2007_Registration, | ||
- | author = {T. Pock and M. Urschler and C. Zach and R. Beichel and H. Bischof}, | + | author = {T. Pock and paperM. Urschler and C. Zach and R. Beichel and H. Bischof}, |
title = {A Duality Based Algorithm for TV-L1-Optical-Flow Image Registration}, | title = {A Duality Based Algorithm for TV-L1-Optical-Flow Image Registration}, | ||
OPTcrossref = {}, | OPTcrossref = {}, | ||
Line 1247: | Line 1357: | ||
title = {Algorithmic Differentiation: | title = {Algorithmic Differentiation: | ||
journal = pami, | journal = pami, | ||
- | year = | + | year = |
OPTkey = {}, | OPTkey = {}, | ||
volume = {29}, | volume = {29}, | ||
Line 1274: | Line 1384: | ||
OPTpublisher = {}, | OPTpublisher = {}, | ||
OPTnote = {}, | OPTnote = {}, | ||
- | OPTannote = {}, | + | OPTannote = {},paper |
titleurl = {ad2008.pdf} | titleurl = {ad2008.pdf} | ||
} | } | ||
Line 1296: | Line 1406: | ||
OPTpublisher = {}, | OPTpublisher = {}, | ||
OPTnote = {}, | OPTnote = {}, | ||
- | OPTannote = {}, | + | OPTannote = {},paper |
titleurl = {cvww08seg.pdf} | titleurl = {cvww08seg.pdf} | ||
} | } | ||
Line 1533: | Line 1643: | ||
@inproceedings{Kleinschmidt-et-al-08, | @inproceedings{Kleinschmidt-et-al-08, | ||
author = {O. Kleinschmidt and T. Brox and D. Cremers}, | author = {O. Kleinschmidt and T. Brox and D. Cremers}, | ||
- | title = {Nonlocal | + | title = {Nonlocal |
booktitle = {Int. Workshop on Local and Nonlocal Approximation}, | booktitle = {Int. Workshop on Local and Nonlocal Approximation}, | ||
month = aug, | month = aug, | ||
Line 2873: | Line 2983: | ||
booktitle = {Algebraic {F}rames for the {P}erception-{A}ction {C}ycle}, | booktitle = {Algebraic {F}rames for the {P}erception-{A}ction {C}ycle}, | ||
year = {2000}, | year = {2000}, | ||
- | editor = {C. Sommer and Y.Y. Zeevi}, | + | editor = {G. Sommer and Y.Y. Zeevi}, |
volume = {1888}, | volume = {1888}, | ||
series = lncs, | series = lncs, | ||
Line 3464: | Line 3574: | ||
} | } | ||
+ | |||
+ | @article{DBLP: | ||
+ | author | ||
+ | S. Caelles and | ||
+ | Y. Chen and | ||
+ | J. Pont{-}Tuset and | ||
+ | L. Leal{-}Taix{\' | ||
+ | D. Cremers and | ||
+ | L. Van Gool}, | ||
+ | title = {Video Object Segmentation without Temporal Information}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1515--1530}, | ||
+ | year = {2019}, | ||
+ | url = {https:// | ||
+ | doi = {10.1109/ | ||
+ | timestamp = {Sat, 30 May 2020 01:00:00 +0200}, | ||
+ | biburl | ||
+ | bibsource = {dblp computer science bibliography, | ||
+ | } | ||
@INPROCEEDINGS{GM04: | @INPROCEEDINGS{GM04: | ||
Line 4351: | Line 4482: | ||
YEAR = " | YEAR = " | ||
MONTH = " | MONTH = " | ||
- | keywords=" | + | keywords=" |
titleurl=" | titleurl=" | ||
titleurl=" | titleurl=" | ||
Line 4365: | Line 4496: | ||
YEAR = " | YEAR = " | ||
MONTH = " | MONTH = " | ||
- | keywords=" | + | keywords=" |
titleurl=" | titleurl=" | ||
} | } | ||
Line 4489: | Line 4620: | ||
booktitle={Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV)}, | booktitle={Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV)}, | ||
year={2011}, | year={2011}, | ||
- | keywords={dense visual odometry, | + | keywords={dense visual odometry, |
} | } | ||
Line 4549: | Line 4680: | ||
month={May}, | month={May}, | ||
year={2012}, | year={2012}, | ||
- | keywords=" | + | keywords=" |
} | } | ||
Line 4678: | Line 4809: | ||
volume = {60}, | volume = {60}, | ||
pages = {270--278} | pages = {270--278} | ||
+ | keywords = {vslam}, | ||
} | } | ||
Line 4762: | Line 4894: | ||
Number = {1}, | Number = {1}, | ||
Title = {Car detection by fusion of HOG and causal MRF}, | Title = {Car detection by fusion of HOG and causal MRF}, | ||
- | Volume | + | volume |
+ | number | ||
+ | pages = {575--590}, | ||
Year = {2015}, | Year = {2015}, | ||
- | | + | } |
+ | |||
+ | @inproceedings{HenschelLCR18, | ||
+ | author | ||
+ | L. Leal{-}Taix{\' | ||
+ | D. Cremers and | ||
+ | B. Rosenhahn}, | ||
+ | title = {Fusion of Head and Full-Body Detectors for Multi-Object Tracking}, | ||
+ | booktitle = {2018 {IEEE} Conference on Computer Vision and Pattern Recognition | ||
+ | | ||
+ | | ||
+ | | ||
+ | publisher = {{IEEE} Computer Society}, | ||
+ | year = {2018}, | ||
} | } | ||
Line 4852: | Line 4999: | ||
year = " | year = " | ||
month= " | month= " | ||
- | keywords=" | + | keywords=" |
} | } | ||
Line 4861: | Line 5008: | ||
year = " | year = " | ||
month= " | month= " | ||
- | keywords=" | + | keywords=" |
} | } | ||
Line 4870: | Line 5017: | ||
year = " | year = " | ||
month= " | month= " | ||
- | keywords=" | + | keywords=" |
} | } | ||
Line 4898: | Line 5045: | ||
year = " | year = " | ||
month= " | month= " | ||
- | keywords=" | + | keywords=" |
} | } | ||
Line 4908: | Line 5055: | ||
year | year | ||
month | month | ||
- | keywords={quadrocopter, | + | keywords={quadrocopter, |
award = {Distinguished with the SIEMENS award for best Master' | award = {Distinguished with the SIEMENS award for best Master' | ||
} | } | ||
Line 4914: | Line 5061: | ||
@inproceedings{Sprenger-et-al-ismrm14, | @inproceedings{Sprenger-et-al-ismrm14, | ||
author = {T. Sprenger and J.I. Sperl and B. Fernandez and V. Golkov and E.T. Tan and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | author = {T. Sprenger and J.I. Sperl and B. Fernandez and V. Golkov and E.T. Tan and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | ||
- | title = {Novel Acquisition Scheme for Diffusion Kurtosis Imaging Based on Compressed-Sensing Accelerated DSI Yielding Superior Image Quality}, | + | title = {Novel Acquisition Scheme for Diffusion Kurtosis Imaging Based on Compressed-Sensing Accelerated |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2014, | year = 2014, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | ||
Line 4923: | Line 5070: | ||
author = {J.I. Sperl and T. Sprenger and E.T. Tan and V. Golkov and M.I. Menzel and C.J. Hardy and L. Marinelli}, | author = {J.I. Sperl and T. Sprenger and E.T. Tan and V. Golkov and M.I. Menzel and C.J. Hardy and L. Marinelli}, | ||
title = {Total Variation-Regularized Compressed Sensing Reconstruction for Multi-Shell Diffusion Kurtosis Imaging}, | title = {Total Variation-Regularized Compressed Sensing Reconstruction for Multi-Shell Diffusion Kurtosis Imaging}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2014, | year = 2014, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total variation} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total variation} | ||
Line 4931: | Line 5078: | ||
author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, | author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, | ||
title = {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}, | title = {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}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2014, | year = 2014, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual} | ||
Line 4938: | Line 5085: | ||
@inproceedings{Golkov-et-al-ismrm14-semi-joint, | @inproceedings{Golkov-et-al-ismrm14-semi-joint, | ||
author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Haase and D. Cremers and J.I. Sperl}, | author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Haase and D. Cremers and J.I. Sperl}, | ||
- | title = {Semi-Joint Reconstruction for Diffusion MRI Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images}, | + | title = {Semi-Joint Reconstruction for Diffusion |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2014, | year = 2014, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | ||
+ | } | ||
+ | |||
+ | @inproceedings{sommer18joint, | ||
+ | author | ||
+ | D. Cremers}, | ||
+ | title = {Joint Representation of Primitive and Non-primitive Objects for 3D | ||
+ | | ||
+ | booktitle = {2018 International Conference on 3D Vision, 3DV 2018, Verona, Italy, | ||
+ | | ||
+ | pages = {160--169}, | ||
+ | publisher = {{IEEE} Computer Society}, | ||
+ | doi = {10.1109/ | ||
+ | year = {2018}, | ||
+ | keywords = {Geometry Processing, SLAM} | ||
} | } | ||
@inproceedings{Golkov-et-al-ohbm14, | @inproceedings{Golkov-et-al-ohbm14, | ||
author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, | author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, | ||
- | title = {Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using 6-D Compressed Sensing}, | + | title = {Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using {6-D} Compressed Sensing}, |
booktitle = ohbm, | booktitle = ohbm, | ||
year = 2014, | year = 2014, | ||
Line 4955: | Line 5116: | ||
author = {V. Golkov and J.I. Sperl and M.I. Menzel and T. Sprenger and E.T. Tan and L. Marinelli and C.J. Hardy and A. Haase and D. Cremers}, | author = {V. Golkov and J.I. Sperl and M.I. Menzel and T. Sprenger and E.T. Tan and L. Marinelli and C.J. Hardy and A. Haase and D. Cremers}, | ||
title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per {q}-Space Coordinate}, | title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per {q}-Space Coordinate}, | ||
- | booktitle = {Computational Diffusion MRI}, | + | booktitle = {Computational Diffusion |
publisher = {Springer}, | publisher = {Springer}, | ||
year = {2014}, | year = {2014}, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, total generalized variation, super-resolution, | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, total generalized variation, super-resolution, | ||
- | award = {Book Chapter, and Oral Presentation at {MICCAI} 2014 Workshop on Computational Diffusion MRI} | + | award = {Book Chapter, and Oral Presentation at {MICCAI} 2014 Workshop on Computational Diffusion |
} | } | ||
@inproceedings{Golkov-et-al-esmrmb13-comparison, | @inproceedings{Golkov-et-al-esmrmb13-comparison, | ||
author = {V. Golkov and T. Sprenger and A. Menini and M.I. Menzel and D. Cremers and J.I. Sperl}, | author = {V. Golkov and T. Sprenger and A. Menini and M.I. Menzel and D. Cremers and J.I. Sperl}, | ||
- | title = {Effects of Low-Rank Constraints, | + | title = {Effects of Low-Rank Constraints, |
- | booktitle = esmrmb, | + | booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, | ||
Line 4973: | Line 5134: | ||
@inproceedings{Golkov-et-al-esmrmb13-iic-nnc, | @inproceedings{Golkov-et-al-esmrmb13-iic-nnc, | ||
author = {V. Golkov and T. Sprenger and M.I. Menzel and D. Cremers and J.I. Sperl}, | author = {V. Golkov and T. Sprenger and M.I. Menzel and D. Cremers and J.I. Sperl}, | ||
- | title = {Line-Process-Based Joint SENSE Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction}, | + | title = {Line-Process-Based Joint {SENSE} Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction}, |
- | booktitle = esmrmb, | + | booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, | ||
Line 4982: | Line 5143: | ||
@inproceedings{Golkov-et-al-dsismrm13, | @inproceedings{Golkov-et-al-dsismrm13, | ||
author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, | author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, | ||
- | title = {Reconstruction, | + | title = {Reconstruction, |
- | booktitle = {16th Annual Meeting of the German Chapter of the ISMRM}, | + | booktitle = {16th Annual Meeting of the German Chapter of the {ISMRM}}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, | ||
Line 4991: | Line 5152: | ||
@inproceedings{Golkov-et-al-podstrana13, | @inproceedings{Golkov-et-al-podstrana13, | ||
author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, | author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, | ||
- | title = {Corrected Joint SENSE Reconstruction, | + | title = {Corrected Joint {SENSE} Reconstruction, |
booktitle = {{ISMRM} Workshop on Diffusion as a Probe of Neural Tissue Microstructure}, | booktitle = {{ISMRM} Workshop on Diffusion as a Probe of Neural Tissue Microstructure}, | ||
year = 2013, | year = 2013, | ||
Line 4999: | Line 5160: | ||
@inproceedings{Sprenger-et-al-ismrm13, | @inproceedings{Sprenger-et-al-ismrm13, | ||
author = {T. Sprenger and B. Fernandez and J.I. Sperl and V. Golkov and M. Bach and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | author = {T. Sprenger and B. Fernandez and J.I. Sperl and V. Golkov and M. Bach and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | ||
- | title = {SNR-dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom}, | + | title = {{SNR}-dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom}, |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | ||
Line 5008: | Line 5169: | ||
author = {J.I. Sperl and E.T. Tan and T. Sprenger and V. Golkov and K.F. King and C.J. Hardy and L. Marinelli and M.I. Menzel}, | author = {J.I. Sperl and E.T. Tan and T. Sprenger and V. Golkov and K.F. King and C.J. Hardy and L. Marinelli and M.I. Menzel}, | ||
title = {Phase Sensitive Reconstruction in Diffusion Spectrum Imaging Enabling Velocity Encoding and Unbiased Noise Distribution}, | title = {Phase Sensitive Reconstruction in Diffusion Spectrum Imaging Enabling Velocity Encoding and Unbiased Noise Distribution}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | ||
Line 5015: | Line 5176: | ||
@inproceedings{Golkov-et-al-ismrm13, | @inproceedings{Golkov-et-al-ismrm13, | ||
author = {V. Golkov and T. Sprenger and M.I. Menzel and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and D. Cremers and J.I. Sperl}, | author = {V. Golkov and T. Sprenger and M.I. Menzel and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and D. Cremers and J.I. Sperl}, | ||
- | title = {Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of SENSE Reconstruction into Joint Reconstruction in Combination with {q}-Space Compressed Sensing}, | + | title = {Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of {SENSE} Reconstruction into Joint Reconstruction in Combination with {q}-Space Compressed Sensing}, |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2013, | year = 2013, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | ||
Line 5023: | Line 5184: | ||
@inproceedings{Sprenger-et-al-esmrmb12, | @inproceedings{Sprenger-et-al-esmrmb12, | ||
author = {T. Sprenger and B. Fernandez and M. Bach and J.I. Sperl and V. Golkov and E.T. Tan and L. Marinelli and K.F. King and C.J. Hardy and Q. Zhu and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | author = {T. Sprenger and B. Fernandez and M. Bach and J.I. Sperl and V. Golkov and E.T. Tan and L. Marinelli and K.F. King and C.J. Hardy and Q. Zhu and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, | ||
- | title = {Evaluation of DSI Imaging with Compressed Sensing under the Presence of Different Noise Levels on a Diffusion Phantom}, | + | title = {Evaluation of {DSI} Imaging with Compressed Sensing under the Presence of Different Noise Levels on a Diffusion Phantom}, |
- | booktitle = esmrmb, | + | booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, |
year = 2012, | year = 2012, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing} | ||
Line 5032: | Line 5193: | ||
author = {V. Golkov and J.I. Sperl and T. Sprenger and H.-J. Bungartz and M. Sedlacek and E.T. Tan and L. Marinelli and C.J. Hardy and K.F. King and M.I. Menzel}, | author = {V. Golkov and J.I. Sperl and T. Sprenger and H.-J. Bungartz and M. Sedlacek and E.T. Tan and L. Marinelli and C.J. Hardy and K.F. King and M.I. Menzel}, | ||
title = {Comparison of Diffusion Kurtosis Tensor Estimation Methods in an Advanced Quality Assessment Framework}, | title = {Comparison of Diffusion Kurtosis Tensor Estimation Methods in an Advanced Quality Assessment Framework}, | ||
- | booktitle = esmrmb, | + | booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, |
year = 2012, | year = 2012, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | ||
Line 5118: | Line 5279: | ||
year | year | ||
month | month | ||
- | keywords={quadrocopter, | + | keywords={quadrocopter, |
} | } | ||
Line 5127: | Line 5288: | ||
year={2012}, | year={2012}, | ||
month={June}, | month={June}, | ||
- | keywords={quadrocopter, | + | keywords={quadrocopter, |
award={Distinguished with the TUM TeachInf Award for the best lecture in summer term 2012} | award={Distinguished with the TUM TeachInf Award for the best lecture in summer term 2012} | ||
} | } | ||
Line 5137: | Line 5298: | ||
year={2013}, | year={2013}, | ||
month={June}, | month={June}, | ||
- | keywords={quadrocopter}, | + | keywords={quadrocopter,vslam}, |
award={Distinguished with the TUM TeachInf Award for the best lecture in summer term 2013} | award={Distinguished with the TUM TeachInf Award for the best lecture in summer term 2013} | ||
} | } | ||
Line 5149: | Line 5310: | ||
award = {Best Vision Paper Award - Finalist}, | award = {Best Vision Paper Award - Finalist}, | ||
- | keywords={dense visual odometry, | + | keywords={dense visual odometry, |
} | } | ||
Line 5330: | Line 5491: | ||
@InProceedings{triebel07collective, | @InProceedings{triebel07collective, | ||
author = {R. Triebel and Ó. Martínez Mozos and W. Burgard}, | author = {R. Triebel and Ó. Martínez Mozos and W. Burgard}, | ||
- | title = {Collective Classification for Labeling of Places and Objects in 2D and 3D Range Data}, | + | title = {Collective Classification for Labeling of Places and Objects in {2D and 3D} Range Data}, |
booktitle = {Proc. of the 31th Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications}, | booktitle = {Proc. of the 31th Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications}, | ||
year = 2007} | year = 2007} | ||
Line 5362: | Line 5523: | ||
@InProceedings{triebel07instance, | @InProceedings{triebel07instance, | ||
author = {R. Triebel and R. Schmidt and Ó. Martínez Mozos and W. Burgard}, | author = {R. Triebel and R. Schmidt and Ó. Martínez Mozos and W. Burgard}, | ||
- | title = {Instance-based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data}, | + | title = {Instance-based AMN Classification for Improved Object Recognition in {2D and 3D} Laser Range Data}, |
booktitle = {Proc. of the International Joint Conference on Artificial Intelligence (IJCAI)}, | booktitle = {Proc. of the International Joint Conference on Artificial Intelligence (IJCAI)}, | ||
year = 2007} | year = 2007} | ||
Line 5413: | Line 5574: | ||
title = {Event-based 3D SLAM with a depth-augmented dynamic vision sensor}, | title = {Event-based 3D SLAM with a depth-augmented dynamic vision sensor}, | ||
booktitle = icra, | booktitle = icra, | ||
+ | keywords={vslam}, | ||
year = 2014} | year = 2014} | ||
Line 5452: | Line 5614: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
} | } | ||
Line 5461: | Line 5623: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
} | } | ||
Line 5528: | Line 5690: | ||
booktitle = {Proc. of the Int. Conf. on Intelligent Robot Systems (IROS)}, | booktitle = {Proc. of the Int. Conf. on Intelligent Robot Systems (IROS)}, | ||
year = 2013, | year = 2013, | ||
- | keywords={dense visual odometry, | + | keywords={dense visual odometry, |
} | } | ||
Line 5561: | Line 5723: | ||
pages = " | pages = " | ||
year = 2013, | year = 2013, | ||
+ | keywords={vslam}, | ||
} | } | ||
Line 5571: | Line 5734: | ||
month={September}, | month={September}, | ||
BOOKTITLE = " | BOOKTITLE = " | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
} | } | ||
Line 5620: | Line 5783: | ||
month={December}, | month={December}, | ||
booktitle = iccv, | booktitle = iccv, | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
} | } | ||
Line 5682: | Line 5845: | ||
publisher = {Springer}, | publisher = {Springer}, | ||
year = 2013 | year = 2013 | ||
+ | } | ||
+ | |||
+ | @inproceedings{KuschkBC17, | ||
+ | author | ||
+ | A. Bozic and | ||
+ | D. Cremers}, | ||
+ | title = {Real-time variational stereo reconstruction with applications to large-scale | ||
+ | dense {SLAM}}, | ||
+ | booktitle = {{IEEE} Intelligent Vehicles Symposium, {IV} 2017, Los Angeles, CA, | ||
+ | USA, June 11-14, 2017}, | ||
+ | pages = {1348--1355}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2017}, | ||
+ | keywords | ||
} | } | ||
Line 5702: | Line 5879: | ||
year | year | ||
month | month | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
} | } | ||
Line 5712: | Line 5889: | ||
year = {2008}, | year = {2008}, | ||
month | month | ||
+ | } | ||
+ | |||
+ | @article{DuranMSC16, | ||
+ | author | ||
+ | M. M{\" | ||
+ | C. Sbert and | ||
+ | D. Cremers}, | ||
+ | title = {On the Implementation of Collaborative {TV} Regularization: | ||
+ | to Cartoon+Texture Decomposition}, | ||
+ | journal | ||
+ | volume | ||
+ | pages = {27--74}, | ||
+ | year = {2016}, | ||
+ | } | ||
+ | |||
+ | @article{DuranMSC16, | ||
+ | author | ||
+ | M. M{\" | ||
+ | C. Sbert and | ||
+ | D. Cremers}, | ||
+ | title = {Collaborative Total Variation: {A} General Framework for Vectorial | ||
+ | {TV} Models}, | ||
+ | titleurl = {Duran_et_al_siims2016.pdf}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {116--151}, | ||
+ | year = {2016}, | ||
+ | } | ||
+ | |||
+ | @article{BurgerGMEC16, | ||
+ | author | ||
+ | G. Gilboa and | ||
+ | M. M{\" | ||
+ | L. Eckardt and | ||
+ | D. Cremers}, | ||
+ | title = {Spectral Decompositions Using One-Homogeneous Functionals}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1374--1408}, | ||
+ | year = {2016}, | ||
} | } | ||
Line 5752: | Line 5971: | ||
titleurl = {steinbruecker_etal_iccv2013.pdf}, | titleurl = {steinbruecker_etal_iccv2013.pdf}, | ||
topic = {3D Reconstruction}, | topic = {3D Reconstruction}, | ||
- | keywords = {RGB-D, | + | keywords = {RGB-D, |
} | } | ||
Line 5796: | Line 6015: | ||
titleurl = {steinbruecker_etal_icra2014.pdf}, | titleurl = {steinbruecker_etal_icra2014.pdf}, | ||
topic = {3D Reconstruction}, | topic = {3D Reconstruction}, | ||
- | keywords = {RGB-D, | + | keywords = {RGB-D, |
} | } | ||
Line 5808: | Line 6027: | ||
pages = " | pages = " | ||
topic = {quadrocopter, | topic = {quadrocopter, | ||
- | keywords = {quadrocopter, | + | keywords = {quadrocopter, |
} | } | ||
Line 5828: | Line 6047: | ||
titleurl = {kee-cvpr14.pdf}, | titleurl = {kee-cvpr14.pdf}, | ||
topic = {Image Segmentation, | topic = {Image Segmentation, | ||
+ | } | ||
+ | |||
+ | @article{KeeLSCK17, | ||
+ | author | ||
+ | Y. Lee and | ||
+ | M. Souiai and | ||
+ | D. Cremers and | ||
+ | J. Kim}, | ||
+ | title = {Sequential Convex Programming for Computing Information-Theoretic | ||
+ | | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1845--1877}, | ||
+ | year = {2017}, | ||
} | } | ||
Line 5836: | Line 6070: | ||
| | ||
Year = {2014}, | Year = {2014}, | ||
+ | | ||
} | } | ||
Line 5850: | Line 6085: | ||
titleurl = {rodola-cgf14.pdf}, | titleurl = {rodola-cgf14.pdf}, | ||
titleurl = {consensus_demo.zip}, | titleurl = {consensus_demo.zip}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{Cremers17, | ||
+ | author | ||
+ | title = {Direct methods for 3D reconstruction and visual {SLAM}}, | ||
+ | booktitle = {Fifteenth {IAPR} International Conference on Machine Vision Applications, | ||
+ | {MVA} 2017, Nagoya, Japan, May 8-12, 2017}, | ||
+ | pages = {34--38}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2017}, | ||
+ | keywords = {vslam}, | ||
} | } | ||
Line 5858: | Line 6104: | ||
month={September}, | month={September}, | ||
booktitle = eccv, | booktitle = eccv, | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
award = {Oral Presentation} | award = {Oral Presentation} | ||
} | } | ||
Line 5868: | Line 6114: | ||
month={September}, | month={September}, | ||
booktitle = ismar, | booktitle = ismar, | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
award = {Best Short Paper Award} | award = {Best Short Paper Award} | ||
} | } | ||
Line 5923: | Line 6169: | ||
address={M\" | address={M\" | ||
month={September}, | month={September}, | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
note = {{<a href="/ | note = {{<a href="/ | ||
award = {Oral Presentation} | award = {Oral Presentation} | ||
Line 6037: | Line 6283: | ||
year | year | ||
month = {May}, | month = {May}, | ||
- | | + | |
} | } | ||
Line 6048: | Line 6294: | ||
year | year | ||
month = {Aug.}, | month = {Aug.}, | ||
- | | + | |
} | } | ||
Line 6057: | Line 6303: | ||
booktitle = {IROS2014 Aerial Open Source Robotics Workshop}, | booktitle = {IROS2014 Aerial Open Source Robotics Workshop}, | ||
year = {2014}, | year = {2014}, | ||
- | | + | |
} | } | ||
Line 6087: | Line 6333: | ||
year | year | ||
month = {Sept.}, | month = {Sept.}, | ||
- | | + | |
} | } | ||
Line 6173: | Line 6419: | ||
number = {4}, | number = {4}, | ||
pages = {809--822}, | pages = {809--822}, | ||
+ | keywords = {rgb-d, 3d reconstruction, | ||
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 6235: | Line 6482: | ||
year = {2014}, | year = {2014}, | ||
doi = {10.1016/ | doi = {10.1016/ | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6362: | Line 6610: | ||
month = jul, | month = jul, | ||
year = {2014}, | year = {2014}, | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6372: | Line 6621: | ||
month = jun, | month = jun, | ||
year = {2014}, | year = {2014}, | ||
+ | keywords = {vo, vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6395: | Line 6645: | ||
pages={5221-5226}, | pages={5221-5226}, | ||
doi={10.1109/ | doi={10.1109/ | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6408: | Line 6659: | ||
doi = {10.1145/ | doi = {10.1145/ | ||
| | ||
- | url = {http:// | + | url = {http:// |
} | } | ||
Line 6419: | Line 6670: | ||
pages={1-6}, | pages={1-6}, | ||
doi={10.1109/ | doi={10.1109/ | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6437: | Line 6689: | ||
publisher = {IEEE}, | publisher = {IEEE}, | ||
year = 2013, | year = 2013, | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6466: | Line 6719: | ||
booktitle = {Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)}, | booktitle = {Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)}, | ||
publisher = {IJCAI/ | publisher = {IJCAI/ | ||
- | year = 2013, | + | year = {2013}, |
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6511: | Line 6765: | ||
pages={162-167}, | pages={162-167}, | ||
doi={10.1109/ | doi={10.1109/ | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6529: | Line 6784: | ||
title = {{SURE}: Surface Entropy for Distinctive 3D Features}, | title = {{SURE}: Surface Entropy for Distinctive 3D Features}, | ||
booktitle = {Proc. of Spatial Cognition}, | booktitle = {Proc. of Spatial Cognition}, | ||
- | year = 2012, | + | year = {2012}, |
url = {http:// | url = {http:// | ||
} | } | ||
Line 6577: | Line 6832: | ||
publisher = {VDE-Verlag}, | publisher = {VDE-Verlag}, | ||
year = 2012, | year = 2012, | ||
+ | keywords = {vslam}, | ||
url = {http:// | url = {http:// | ||
} | } | ||
Line 6751: | Line 7007: | ||
doi={10.1109/ | doi={10.1109/ | ||
url={http:// | url={http:// | ||
+ | keywords = {vslam}, | ||
} | } | ||
Line 6838: | Line 7095: | ||
year={2015}, | year={2015}, | ||
note={to appear}, | note={to appear}, | ||
+ | keywords = {vslam}, | ||
} | } | ||
Line 6927: | Line 7185: | ||
@inproceedings{Gomez-et-al-ismrm15, | @inproceedings{Gomez-et-al-ismrm15, | ||
author = {P.A. G\' | author = {P.A. G\' | ||
- | title = {Using Diffusion and Structural MRI for the Automated Segmentation of Multiple Sclerosis Lesions}, | + | title = {Using Diffusion and Structural |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2015, | year = 2015, | ||
keywords = {magnetic resonance imaging, diffusion MRI, segmentation, | keywords = {magnetic resonance imaging, diffusion MRI, segmentation, | ||
Line 6936: | Line 7194: | ||
author = {M.I. Menzel and T. Sprenger and E.T. Tan and V. Golkov and C.J. Hardy and L. Marinelli and J.I. Sperl}, | author = {M.I. Menzel and T. Sprenger and E.T. Tan and V. Golkov and C.J. Hardy and L. Marinelli and J.I. Sperl}, | ||
title = {Robustness of Phase Sensitive Reconstruction in Diffusion Spectrum Imaging}, | title = {Robustness of Phase Sensitive Reconstruction in Diffusion Spectrum Imaging}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2015, | year = 2015, | ||
keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | keywords = {magnetic resonance imaging, diffusion MRI, medical imaging} | ||
Line 6943: | Line 7201: | ||
@inproceedings{Menini-et-al-ismrm15, | @inproceedings{Menini-et-al-ismrm15, | ||
author = {A. Menini and V. Golkov and F. Wiesinger}, | author = {A. Menini and V. Golkov and F. Wiesinger}, | ||
- | title = {Free-Breathing, | + | title = {Free-Breathing, |
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
year = 2015, | year = 2015, | ||
keywords = {magnetic resonance imaging, medical imaging} | keywords = {magnetic resonance imaging, medical imaging} | ||
Line 6951: | Line 7209: | ||
@inproceedings{Golkov-et-al-miccai2015-qDL, | @inproceedings{Golkov-et-al-miccai2015-qDL, | ||
author = {V. Golkov and A. Dosovitskiy and P. S\" | author = {V. Golkov and A. Dosovitskiy and P. S\" | ||
- | title = {{q}-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, | + | title = {{q-Space} Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion |
booktitle = miccai, | booktitle = miccai, | ||
month = oct, | month = oct, | ||
year = 2015, | year = 2015, | ||
address = {Munich, Germany}, | address = {Munich, Germany}, | ||
- | keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, | + | keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, |
} | } | ||
Line 6965: | Line 7223: | ||
title = " | title = " | ||
booktitle = iccv, | booktitle = iccv, | ||
- | keywords = {deeplearning, optical-flow}, | + | keywords = {deep learning, optical-flow}, |
year = 2015, | year = 2015, | ||
month = dec, | month = dec, | ||
Line 6974: | Line 7232: | ||
@incollection{Golkov-et-al-cdmri2015-holistic, | @incollection{Golkov-et-al-cdmri2015-holistic, | ||
author = {V. Golkov and J. M. Portegies and A. Golkov and R. Duits and D. Cremers}, | author = {V. Golkov and J. M. Portegies and A. Golkov and R. Duits and D. Cremers}, | ||
- | title = {Holistic Image Reconstruction for Diffusion MRI}, | + | title = {Holistic Image Reconstruction for Diffusion |
- | booktitle = {Computational Diffusion MRI}, | + | booktitle = {Computational Diffusion |
month = oct, | month = oct, | ||
publisher = {Springer}, | publisher = {Springer}, | ||
Line 6981: | Line 7239: | ||
address = {Munich, Germany}, | address = {Munich, Germany}, | ||
keywords = {magnetic resonance imaging, diffusion MRI, primal-dual, | keywords = {magnetic resonance imaging, diffusion MRI, primal-dual, | ||
- | award = {Book Chapter, and Oral Presentation at {MICCAI} 2015 Workshop on Computational Diffusion MRI} | + | award = {Book Chapter, and Oral Presentation at {MICCAI} 2015 Workshop on Computational Diffusion |
} | } | ||
Line 7012: | Line 7270: | ||
year = 2015, | year = 2015, | ||
month = sept, | month = sept, | ||
- | keywords = {slam, stereo, semidense, reconstruction} | + | keywords = {slam, stereo, semidense, reconstruction, vslam} |
} | } | ||
Line 7021: | Line 7279: | ||
month = sept, | month = sept, | ||
year = 2015, | year = 2015, | ||
- | keywords = {slam, fisheye, semidense, reconstruction} | + | keywords = {slam, fisheye, semidense, reconstruction, vslam} |
} | } | ||
Line 7048: | Line 7306: | ||
month = oct, | month = oct, | ||
year = 2015, | year = 2015, | ||
- | keywords = {slam, stereo, semidense, reconstruction} | + | keywords = {slam, stereo, semidense, reconstruction, vslam} |
} | } | ||
Line 7103: | Line 7361: | ||
year = {2015}, | year = {2015}, | ||
address = {Santiago, Chile}, | address = {Santiago, Chile}, | ||
- | keywords = {rgb-d, | + | keywords = {rgb-d, |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7116: | Line 7374: | ||
keywords = {convex relaxation, | keywords = {convex relaxation, | ||
} | } | ||
+ | |||
+ | @article{KeeLYCK15, | ||
+ | author | ||
+ | H. Lee and | ||
+ | J. Yim and | ||
+ | D. Cremers and | ||
+ | J. Kim}, | ||
+ | title = {Entropy Minimization for Groupwise Planar Shape Co-alignment and its | ||
+ | | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1922--1926}, | ||
+ | year = {2015}, | ||
+ | } | ||
+ | |||
@inproceedings{stark-gcpr15, | @inproceedings{stark-gcpr15, | ||
author = {F. Stark and C. Hazirbas and R. Triebel and D. Cremers}, | author = {F. Stark and C. Hazirbas and R. Triebel and D. Cremers}, | ||
Line 7123: | Line 7397: | ||
address = {Aachen, Germany}, | address = {Aachen, Germany}, | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = {deeplearning} | + | keywords = {deep learning} |
} | } | ||
Line 7239: | Line 7513: | ||
author = {E. Rodola and L. Cosmo and M. M. Bronstein and A. Torsello and D. Cremers}, | author = {E. Rodola and L. Cosmo and M. M. Bronstein and A. Torsello and D. Cremers}, | ||
title = {Partial Functional Correspondence}, | title = {Partial Functional Correspondence}, | ||
- | year = {2016}, | + | |
+ | number | ||
+ | pages = {222--236}, | ||
+ | | ||
journal = {Computer Graphics Forum}, | journal = {Computer Graphics Forum}, | ||
publisher = {Wiley}, | publisher = {Wiley}, | ||
Line 7249: | Line 7526: | ||
author = {L. Cosmo and E. Rodola and A. Albarelli and F. Memoli and D. Cremers}, | author = {L. Cosmo and E. Rodola and A. Albarelli and F. Memoli and D. Cremers}, | ||
title = {Consistent Partial Matching of Shape Collections via Sparse Modeling}, | title = {Consistent Partial Matching of Shape Collections via Sparse Modeling}, | ||
- | year = {2016}, | + | year = {2017}, |
+ | volume | ||
+ | number | ||
+ | pages = {209--221}, | ||
journal = {Computer Graphics Forum}, | journal = {Computer Graphics Forum}, | ||
publisher = {Wiley}, | publisher = {Wiley}, | ||
Line 7264: | Line 7544: | ||
volume = {35}, | volume = {35}, | ||
number = {2}, | number = {2}, | ||
+ | pages = {431--441}, | ||
topic = {Shape Analysis, Shape Matching}, | topic = {Shape Analysis, Shape Matching}, | ||
} | } | ||
- | + | @inproceedings{MayerIHFCDB16, | |
- | + | author | |
- | + | E. Ilg and | |
- | @InProceedings{sceneflownet-arxiv, | + | P. H{\" |
- | author | + | P. Fischer and |
- | title = "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation", | + | D. Cremers and |
- | booktitle | + | A. Dosovitskiy and |
- | | + | T. Brox}, |
- | | + | title |
- | | + | Flow, and Scene Flow Estimation}, |
- | | + | booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition, |
- | keywords | + | {CVPR} |
+ | | ||
+ | | ||
+ | | ||
} | } | ||
Line 7292: | Line 7576: | ||
@inproceedings{Golkov-et-al-isbi2016, | @inproceedings{Golkov-et-al-isbi2016, | ||
author = {V. Golkov and T. Sprenger and J. I. Sperl and M. I. Menzel and M. Czisch and P. S\" | author = {V. Golkov and T. Sprenger and J. I. Sperl and M. I. Menzel and M. Czisch and P. S\" | ||
- | title = {Model-Free Novelty-Based Diffusion MRI}, | + | title = {Model-Free Novelty-Based Diffusion |
- | booktitle = isbi, | + | booktitle = {{IEEE} International Symposium on Biomedical Imaging ({ISBI})}, |
month = apr, | month = apr, | ||
year = 2016, | year = 2016, | ||
Line 7307: | Line 7591: | ||
year = 2016, | year = 2016, | ||
address = {Barcelona, Spain}, | address = {Barcelona, Spain}, | ||
- | keywords = {computational structural biology, deep learning, convolutional networks, graph-valued images, | + | keywords = {computational structural biology, deep learning, convolutional networks, graph-valued images, |
note = {{<a href=" | note = {{<a href=" | ||
award = {Oral Presentation (acceptance rate: under 2%)} | award = {Oral Presentation (acceptance rate: under 2%)} | ||
Line 7322: | Line 7606: | ||
@article{Golkov-et-al-tmi2016, | @article{Golkov-et-al-tmi2016, | ||
author = {V. Golkov and A. Dosovitskiy and J. I. Sperl and M. I. Menzel and M. Czisch and P. S\" | author = {V. Golkov and A. Dosovitskiy and J. I. Sperl and M. I. Menzel and M. Czisch and P. S\" | ||
- | title = {{q}-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, | + | title = {{q-Space} Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion |
year = {2016}, | year = {2016}, | ||
journal = tmi, | journal = tmi, | ||
volume = {35}, | volume = {35}, | ||
issue = {5}, | issue = {5}, | ||
- | keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, | + | keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, |
issuetitle = {Special Issue on Deep Learning}, | issuetitle = {Special Issue on Deep Learning}, | ||
award = {Special Issue on Deep Learning} | award = {Special Issue on Deep Learning} | ||
Line 7339: | Line 7623: | ||
month = " | month = " | ||
year = " | year = " | ||
- | | + | |
url = {http:// | url = {http:// | ||
note = {{<a href=" | note = {{<a href=" | ||
Line 7350: | Line 7634: | ||
year = " | year = " | ||
month = " | month = " | ||
- | + | | |
- | | + | |
} | } | ||
Line 7371: | Line 7654: | ||
topic={Shape Analysis}, | topic={Shape Analysis}, | ||
url = {http:// | url = {http:// | ||
- | keywords = {Shape Analysis, Shape Matching, SHREC, Topology}, | + | keywords = {Shape Analysis, Shape Matching, SHREC, Topology, Geometry Processing}, |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7413: | Line 7696: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={RGB-D SLAM, semantic segmentation} | + | keywords={RGB-D SLAM, semantic segmentation, vslam} |
} | } | ||
Line 7447: | Line 7730: | ||
volume = {35}, | volume = {35}, | ||
number = {5}, | number = {5}, | ||
+ | pages = {135--143}, | ||
award = {Received the Best Paper Award at SGP 2016}, | award = {Received the Best Paper Award at SGP 2016}, | ||
year = {2016}, | year = {2016}, | ||
Line 7475: | Line 7759: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={mono-ds, | + | keywords={mono-ds, |
} | } | ||
Line 7486: | Line 7770: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={mono-ds, | + | keywords={mono-ds, |
} | } | ||
Line 7496: | Line 7780: | ||
month = mar, | month = mar, | ||
titleurl = {engel_et_al_pami2018.pdf}, | titleurl = {engel_et_al_pami2018.pdf}, | ||
- | keywords={mono-ds, | + | keywords={mono-ds, |
} | } | ||
Line 7507: | Line 7791: | ||
keywords={convex-optimization, | keywords={convex-optimization, | ||
note = {{<a href="/ | note = {{<a href="/ | ||
+ | } | ||
+ | |||
+ | @inproceedings{BenderCK16, | ||
+ | author | ||
+ | D. Cremers and | ||
+ | W. Koch}, | ||
+ | title = {A position free boresight calibration for INS-camera systems}, | ||
+ | booktitle = {2016 {IEEE} International Conference on Multisensor Fusion and Integration | ||
+ | for Intelligent Systems, {MFI} 2016, Baden-Baden, | ||
+ | | ||
+ | pages = {52--57}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2016}, | ||
} | } | ||
Line 7515: | Line 7812: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={shape retrieval, shape representation, | + | keywords={shape retrieval, shape representation, |
note = {{<a href=" | note = {{<a href=" | ||
+ | keywords = {Geometry Processing} | ||
} | } | ||
Line 7525: | Line 7823: | ||
| | ||
month = november, | month = november, | ||
- | | + | |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7536: | Line 7834: | ||
month = " | month = " | ||
topic = {Shape Analysis, Convex Relaxation Methods, Correspondence}, | topic = {Shape Analysis, Convex Relaxation Methods, Correspondence}, | ||
- | keywords={Shape Analysis, optical-flow, | + | keywords = {Geometry Processing} |
} | } | ||
Line 7557: | Line 7855: | ||
titleurl = {2017_ProductManifoldFilter.pdf}, | titleurl = {2017_ProductManifoldFilter.pdf}, | ||
topic = {Shape Analysis, Shape Matching}, | topic = {Shape Analysis, Shape Matching}, | ||
- | note = {{<a href="/ | + | note = {{<a href="/ |
+ | keywords = {Geometry Processing} | ||
} | } | ||
Line 7583: | Line 7882: | ||
arXiv = {arXiv: | arXiv = {arXiv: | ||
year = {2017}, | year = {2017}, | ||
- | month = {September} | + | month = {September}, |
+ | keywords | ||
+ | note = {{<a href=" | ||
} | } | ||
@InProceedings{walch16spatialstms, | @InProceedings{walch16spatialstms, | ||
- | | + | |
- | | + | |
- | | + | |
- | | + | |
- | | + | |
- | | + | |
title = " | title = " | ||
month = " | month = " | ||
Line 7598: | Line 7899: | ||
| | ||
note = {{<a href=" | note = {{<a href=" | ||
- | | + | |
} | } | ||
Line 7608: | Line 7909: | ||
year={2016}, | year={2016}, | ||
publisher={Springer}, | publisher={Springer}, | ||
- | | + | |
} | } | ||
Line 7629: | Line 7930: | ||
publisher=" | publisher=" | ||
month = " | month = " | ||
- | keywords={autonomous driving, reinforcement learning, artificial intelligence, | + | keywords={autonomous driving, reinforcement learning, artificial intelligence, |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7650: | Line 7951: | ||
year = {2017}, | year = {2017}, | ||
booktitle = {23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO)}, | booktitle = {23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO)}, | ||
- | keywords = {deeplearning, medical imaging} | + | keywords = {deep learning, medical imaging} |
} | } | ||
- | @article{Golkov-et-al-arxiv2017-function3d, | + | @inproceedings{Golkov-et-al-arxiv2017-function3d, |
author = {V. Golkov and M. J. Skwark and A. Mirchev and G. Dikov and A. R. Geanes and J. Mendenhall and J. Meiler and D. Cremers}, | author = {V. Golkov and M. J. Skwark and A. Mirchev and G. Dikov and A. R. Geanes and J. Mendenhall and J. Meiler and D. Cremers}, | ||
- | title = {3D Deep Learning for Biological Function Prediction from Physical Fields}, | + | title = {{3D} Deep Learning for Biological Function Prediction from Physical Fields}, |
- | year = {2017}, | + | booktitle = {International Conference on 3D Vision (3DV)}, |
+ | year = {2020}, | ||
journal = {arXiv preprint arXiv: | journal = {arXiv preprint arXiv: | ||
eprint = {1704.04039}, | eprint = {1704.04039}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {computational structural biology, deep learning, convolutional networks, protein function, QSAR, deeplearning}, | + | keywords = {computational structural biology, deep learning, convolutional networks, protein function, QSAR, deep learning, biology}, |
} | } | ||
Line 7677: | Line 7979: | ||
booktitle = cvpr, | booktitle = cvpr, | ||
year = " | year = " | ||
- | keywords = {semi-supervised, | + | keywords = {semi-supervised, |
titleurl = {haeusser_cvpr_17.pdf}, | titleurl = {haeusser_cvpr_17.pdf}, | ||
note = {{<a href=" | note = {{<a href=" | ||
Line 7684: | Line 7986: | ||
@InProceedings{slavcheva-et-al-cvpr17, | @InProceedings{slavcheva-et-al-cvpr17, | ||
- | author = "Miroslava | + | author = "M. Slavcheva and |
- | Maximilian | + | M. Baust and |
- | Daniel | + | D. Cremers and |
- | Slobodan | + | S. Ilic", |
title = " | title = " | ||
booktitle = cvpr, | booktitle = cvpr, | ||
Line 7717: | Line 8019: | ||
year = {2017}, | year = {2017}, | ||
doi = {10.1007/ | doi = {10.1007/ | ||
+ | } | ||
+ | |||
+ | @inproceedings{BenderRSCK16, | ||
+ | author | ||
+ | F. Rouatbi and | ||
+ | M. Schikora and | ||
+ | D. Cremers and | ||
+ | W. Koch}, | ||
+ | title = {Scaling the world of monocular {SLAM} with INS-measurements for {UAS} | ||
+ | | ||
+ | booktitle = {19th International Conference on Information Fusion, {FUSION} 2016, | ||
+ | | ||
+ | pages = {1493--1500}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2016}, | ||
+ | keywords={vslam} | ||
} | } | ||
Line 7770: | Line 8088: | ||
@InProceedings{meinhardt17learning, | @InProceedings{meinhardt17learning, | ||
- | | + | |
- | Michael | + | M. Moeller and |
- | Caner Hazirbas and | + | C. Hazirbas and |
- | Daniel | + | D. Cremers", |
title = {Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems}, | title = {Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems}, | ||
month = " | month = " | ||
Line 7779: | Line 8097: | ||
| | ||
note = {{<a href=" | note = {{<a href=" | ||
- | | + | |
} | } | ||
@InProceedings{hazirbas18ddff, | @InProceedings{hazirbas18ddff, | ||
- | | + | |
title = {{Deep Depth From Focus}}, | title = {{Deep Depth From Focus}}, | ||
year = " | year = " | ||
Line 7789: | Line 8107: | ||
| | ||
note = {{<a href=" | note = {{<a href=" | ||
- | | + | |
} | } | ||
Line 7798: | Line 8116: | ||
title = {{One-Shot Video Object Segmentation}}, | title = {{One-Shot Video Object Segmentation}}, | ||
year = {2017}, | year = {2017}, | ||
- | keywords = {deeplearning}, | + | keywords = {deep learning}, |
} | } | ||
Line 7821: | Line 8139: | ||
doi = " | doi = " | ||
titleurl = {yang18challenges.pdf}, | titleurl = {yang18challenges.pdf}, | ||
- | keywords={Brightness; | + | keywords={Brightness; |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7827: | Line 8145: | ||
@article{krieg2017genetic, | @article{krieg2017genetic, | ||
title={Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling}, | title={Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling}, | ||
- | author={Krieg, Michael | + | author={M. Krieg and J. St\" |
journal={eLife}, | journal={eLife}, | ||
volume={6}, | volume={6}, | ||
Line 7833: | Line 8151: | ||
year={2017}, | year={2017}, | ||
publisher={eLife Sciences Publications Limited}, | publisher={eLife Sciences Publications Limited}, | ||
- | keywords=biomed | + | keywords={biology} |
} | } | ||
@article{krieg2017tau, | @article{krieg2017tau, | ||
title={Tau Like Proteins Reduce Torque Generation in Microtubule Bundles}, | title={Tau Like Proteins Reduce Torque Generation in Microtubule Bundles}, | ||
- | author={Krieg, Michael | + | author={M. Krieg and J. St\" |
journal={Biophysical Journal}, | journal={Biophysical Journal}, | ||
volume={112}, | volume={112}, | ||
Line 7866: | Line 8184: | ||
year = " | year = " | ||
month = " | month = " | ||
- | keywords={Multimodal, | + | keywords={Multimodal, |
titleurl = {kurach2017.pdf}, | titleurl = {kurach2017.pdf}, | ||
} | } | ||
Line 7875: | Line 8193: | ||
booktitle = iccv, | booktitle = iccv, | ||
year = " | year = " | ||
- | keywords = {semi-supervised, | + | keywords = {semi-supervised, |
titleurl = {haeusser_iccv_17.pdf}, | titleurl = {haeusser_iccv_17.pdf}, | ||
note = {{<a href=" | note = {{<a href=" | ||
Line 7910: | Line 8228: | ||
| | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
- | topic = {Shape Analysis, Shape Matching}, | + | keywords |
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
Line 7923: | Line 8241: | ||
month={September}, | month={September}, | ||
address={London, | address={London, | ||
- | keywords={rgb-d, | + | keywords={rgb-d, |
note = {{<a href="/ | note = {{<a href="/ | ||
+ | } | ||
+ | |||
+ | @inproceedings{GeipingDC017, | ||
+ | author | ||
+ | H. Dirks and | ||
+ | D. Cremers and | ||
+ | M. M{\" | ||
+ | editor | ||
+ | Edwin R. Hancock}, | ||
+ | title = {Multiframe Motion Coupling for Video Super Resolution}, | ||
+ | booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition | ||
+ | - 11th International Conference, {EMMCVPR} 2017, Venice, Italy, October | ||
+ | 30 - November 1, 2017, Revised Selected Papers}, | ||
+ | series | ||
+ | volume | ||
+ | pages = {123--138}, | ||
+ | publisher = {Springer}, | ||
+ | year = {2017}, | ||
} | } | ||
Line 7958: | Line 8294: | ||
year = {2019}, | year = {2019}, | ||
note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | ||
+ | keywords = { Geometry Processing }, | ||
} | } | ||
Line 7967: | Line 8304: | ||
address={Colorado, | address={Colorado, | ||
year = {2020}, | year = {2020}, | ||
+ | doi=" | ||
eprint = {1912.06501}, | eprint = {1912.06501}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
Line 7972: | Line 8310: | ||
award = {Spotlight Presentation}, | award = {Spotlight Presentation}, | ||
titleurl = {sang2020wacv.pdf}, | titleurl = {sang2020wacv.pdf}, | ||
+ | note = { | ||
+ | {<a href="/ | ||
+ | {<a href="/ | ||
+ | {<a href=" | ||
+ | {<a href=" | ||
+ | }, | ||
+ | keywords = {3d-reconstruction, | ||
} | } | ||
- | @article{mukkamala2019arxiv, | + | @inbook{maier2020rgbdvision, |
- | title={Bregman Proximal Framework for Deep Linear Neural Networks}, | + | title={{RGB-D Vision}}, |
- | author={Mahesh Chandra Mukkamala and Felix Westerkamp | + | author={R. Maier and D. Cremers}, |
- | | + | chapter={Encyclopedia of Robotics}, |
- | year = {2019}, | + | editor={Ang, |
- | | + | year={2020}, |
- | | + | publisher={Springer Berlin Heidelberg}, |
- | | + | address={Berlin, |
+ | pages={1--11}, | ||
+ | isbn={978-3-642-41610-1}, | ||
+ | doi={10.1007/ | ||
+ | url={https:// | ||
+ | | ||
} | } | ||
- | @article{brahimi2019springer, | + | @inbook{brahimi2020springer, |
- | title = {On well-posedness | + | title={On |
- | author = {Brahimi, | + | author={Brahimi, |
- | | + | |
- | year = {2019}, | + | booktitle={Advances in Photometric 3D-Reconstruction}, |
+ | year={2020}, | ||
+ | publisher={Springer International Publishing}, | ||
+ | address={Cham}, | ||
+ | pages={147--176}, | ||
+ | isbn={978-3-030-51866-0}, | ||
+ | doi={10.1007/ | ||
eprint = {1911.07268}, | eprint = {1911.07268}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
- | titleurl = {brahimi2019springer.pdf}, | + | |
+ | keywords = {photometry}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{brahimi2022arxiv, | ||
+ | title = {{SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering}}, | ||
+ | author = {Brahimi, Mohammed and Haefner, Bjoern and Yenamandra, Tarun and Goldluecke, Bastian and Cremers, Daniel}, | ||
+ | booktitle={{IEEE Winter Conference on Applications of Computer Vision (WACV)}}, | ||
+ | month={January}, | ||
+ | address={Hawaii, | ||
+ | year = {2024}, | ||
+ | eprint = {2212.04968}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | titleurl = {brahimi2022arxiv.pdf}, | ||
+ | note = | ||
+ | { | ||
+ | {<a href="/ | ||
+ | }, | ||
+ | keywords={d-reconstruction, | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{DykeSLRABBBCFGG19, | ||
+ | author | ||
+ | C. Stride and | ||
+ | | ||
+ | P. L. Rosin and | ||
+ | M. Aubry and | ||
+ | A. Boyarski and | ||
+ | A. M. Bronstein and | ||
+ | M. M. Bronstein and | ||
+ | D. Cremers and | ||
+ | M. Fisher and | ||
+ | T. Groueix and | ||
+ | D. Guo and | ||
+ | V. G. Kim and | ||
+ | R. Kimmel and | ||
+ | Z. L{\" | ||
+ | K. Li and | ||
+ | O. Litany and | ||
+ | T. Remez and | ||
+ | E. Rodol{\`{a}} and | ||
+ | B. C. Russell and | ||
+ | Y. Sahillioglu and | ||
+ | R. Slossberg and | ||
+ | G. K. L. Tam and | ||
+ | M. Vestner and | ||
+ | Z. Wu and | ||
+ | J. Yang}, | ||
+ | editor | ||
+ | | ||
+ | Remco C. Veltkamp}, | ||
+ | title = {Shape Correspondence with Isometric and Non-Isometric Deformations}, | ||
+ | booktitle = {12th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics | ||
+ | 2019, Genoa, Italy, May 5-6, 2019}, | ||
+ | pages = {111--119}, | ||
+ | publisher = {Eurographics Association}, | ||
+ | year = {2019}, | ||
+ | keywords = {Geometry Processing}, | ||
} | } | ||
Line 8002: | Line 8417: | ||
address={Québec City, Canada}, | address={Québec City, Canada}, | ||
year = {2019}, | year = {2019}, | ||
+ | doi=" | ||
award = {Spotlight Presentation}, | award = {Spotlight Presentation}, | ||
titleurl = {haefner20193dv.pdf}, | titleurl = {haefner20193dv.pdf}, | ||
note = { | note = { | ||
{<a href="/ | {<a href="/ | ||
+ | {<a href="/ | ||
+ | }, | ||
+ | keywords = {photometry}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{haefner20213dv, | ||
+ | title = {Recovering Real-world Reflectance Properties and Shading from HDR Imagery}, | ||
+ | author = {B. Haefner and S. Green and A. Oursland and D. Andersen and M. Goesele and D. Cremers and R. Newcombe and T. Whelan}, | ||
+ | booktitle={International Conference on 3D Vision (3DV)}, | ||
+ | year = {2021}, | ||
+ | doi=" | ||
+ | titleurl = {haefner20213dv.pdf}, | ||
+ | award = {Spotlight Presentation}, | ||
+ | note = { | ||
+ | {<a href="/ | ||
+ | {<a href="/ | ||
+ | {<a href="/ | ||
+ | {<a href=" | ||
+ | {<a href=" | ||
}, | }, | ||
+ | keywords = {photometry}, | ||
} | } | ||
Line 8012: | Line 8448: | ||
title = {Variational Uncalibrated Photometric Stereo under General Lighting}, | title = {Variational Uncalibrated Photometric Stereo under General Lighting}, | ||
author = {Haefner, B. and Ye, Z. and Gao, M. and Wu, T. and Quéau, Y. and Cremers, D.}, | author = {Haefner, B. and Ye, Z. and Gao, M. and Wu, T. and Quéau, Y. and Cremers, D.}, | ||
- | booktitle={International Conference on Computer Vision (ICCV)}, | + | booktitle={IEEE/ |
month={October}, | month={October}, | ||
address={Seoul, | address={Seoul, | ||
Line 8019: | Line 8455: | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
year = {2019}, | year = {2019}, | ||
- | titleurl = {haefner2019.pdf}, | + | |
+ | | ||
note = { | note = { | ||
- | {<a href="/ | + | {<a href="/ |
- | {<a href="/ | + | {<a href="/ |
- | {<a href=" | + | {<a href=" |
+ | {<a href=" | ||
{<a href=" | {<a href=" | ||
- | }, | + | |
keywords={d-reconstruction, | keywords={d-reconstruction, | ||
} | } | ||
- | + | @article{laude2020jota, | |
- | @article{laude2019jota, | + | |
title = {Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity}, | title = {Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity}, | ||
| | ||
| | ||
- | note="to appear" | + | volume={184}, |
- | year = {2019}, | + | |
+ | | ||
+ | year = {2020}, | ||
eprint = {1907.04306}, | eprint = {1907.04306}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
Line 8041: | Line 8480: | ||
} | } | ||
- | @article{haefner2019tpami, | + | @article{haefner2020tpami, |
- | title = {Photometric Depth Super-Resolution}, | + | title = {Photometric Depth Super-Resolution}, |
- | | + | author = {Haefner, B. and Peng, S. and Verma, A. and Quéau, Y. and Cremers, D.}, |
- | | + | journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, |
- | year = {2019}, | + | year={2020}, |
+ | volume={42}, | ||
+ | number={10}, | ||
+ | pages={2453-2464}, | ||
+ | doi=" | ||
eprint = {1809.10097}, | eprint = {1809.10097}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
- | titleurl = {haefner2018.pdf}, | + | titleurl = {haefner2020tpami.pdf}, |
note = { | note = { | ||
- | {<a href="/ | + | {<a href="/ |
{<a href=" | {<a href=" | ||
- | {<a href=" | ||
}, | }, | ||
keywords={rgb-d, | keywords={rgb-d, | ||
+ | } | ||
+ | |||
+ | @article{MoellerBSC15, | ||
+ | author | ||
+ | M. Benning and | ||
+ | C. Sch{\" | ||
+ | D. Cremers}, | ||
+ | title = {Variational Depth From Focus Reconstruction}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {5369--5378}, | ||
+ | year = {2015}, | ||
} | } | ||
Line 8062: | Line 8517: | ||
title = {Fight ill-posedness with ill-posedness: | title = {Fight ill-posedness with ill-posedness: | ||
| | ||
- | | + | |
(CVPR)}, | (CVPR)}, | ||
year = {2018}, | year = {2018}, | ||
+ | doi = " | ||
| | ||
note = { | note = { | ||
{<a href="/ | {<a href="/ | ||
{<a href="/ | {<a href="/ | ||
+ | {<a href="/ | ||
{<a href=" | {<a href=" | ||
{<a href=" | {<a href=" | ||
Line 8081: | Line 8538: | ||
title = {Depth Super-Resolution Meets Uncalibrated Photometric Stereo}, | title = {Depth Super-Resolution Meets Uncalibrated Photometric Stereo}, | ||
| | ||
- | | + | |
year = {2017}, | year = {2017}, | ||
+ | | ||
eprint = {1708.00411}, | eprint = {1708.00411}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
Line 8089: | Line 8547: | ||
| | ||
note = { | note = { | ||
- | {<a href=" | + | {<a href=" |
{<a href=" | {<a href=" | ||
}, | }, | ||
Line 8095: | Line 8553: | ||
} | } | ||
+ | |||
+ | @INPROCEEDINGS{yen2019SDPIK, | ||
+ | author={T. Yenamandra and F. Bernard and J. Wang and F. Mueller and C. Theobalt}, | ||
+ | booktitle={2019 International Conference on 3D Vision (3DV)}, | ||
+ | title={Convex Optimisation for Inverse Kinematics}, | ||
+ | year={2019}, | ||
+ | pages={318-327}, | ||
+ | award = {Oral Presentation}} | ||
@inproceedings{wang2017stereoDSO, | @inproceedings{wang2017stereoDSO, | ||
Line 8169: | Line 8635: | ||
note = {{<a href=" | note = {{<a href=" | ||
doi = {10.1007/ | doi = {10.1007/ | ||
+ | } | ||
+ | |||
+ | @article{RodolaMC17, | ||
+ | author | ||
+ | | ||
+ | | ||
+ | title = {Regularized Pointwise Map Recovery from Functional Correspondence}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {700--711}, | ||
+ | year = {2017}, | ||
+ | keywords = {Geometry Processing} | ||
+ | } | ||
+ | |||
+ | @article{Cremers17, | ||
+ | author | ||
+ | title = {Computer Vision f{\" | ||
+ | | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {205--209}, | ||
+ | year = {2017}, | ||
+ | } | ||
+ | |||
+ | @article{BringmannCKM18, | ||
+ | author | ||
+ | | ||
+ | Felix Krahmer and | ||
+ | | ||
+ | title = {The homotopy method revisited: Computing solution paths of L1-regularized | ||
+ | | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {2343--2364}, | ||
+ | year = {2018}, | ||
} | } | ||
Line 8175: | Line 8679: | ||
title = {{Variational Reflectance Estimation from Multi-view Images}}, | title = {{Variational Reflectance Estimation from Multi-view Images}}, | ||
doi = {10.1007/ | doi = {10.1007/ | ||
+ | volume | ||
+ | number | ||
+ | pages = {1527--1546}, | ||
year = " | year = " | ||
| | ||
Line 8213: | Line 8720: | ||
year = " | year = " | ||
titleurl = {laude-2018-discrete-continuous.pdf} | titleurl = {laude-2018-discrete-continuous.pdf} | ||
+ | } | ||
+ | |||
+ | @inproceedings{DomokosSC18, | ||
+ | author | ||
+ | Frank R. Schmidt and | ||
+ | | ||
+ | editor | ||
+ | | ||
+ | | ||
+ | Yair Weiss}, | ||
+ | title = {{MRF} Optimization with Separable Convex Prior on Partially Ordered | ||
+ | | ||
+ | booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich, | ||
+ | | ||
+ | series | ||
+ | volume | ||
+ | pages = {341--356}, | ||
+ | publisher = {Springer}, | ||
+ | year = {2018}, | ||
} | } | ||
Line 8237: | Line 8763: | ||
title = {A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching}, | title = {A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching}, | ||
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
- | year = {2017} | + | year = {2017}, |
+ | keywords = {Geometry Processing} | ||
} | } | ||
+ | |||
+ | @inproceedings{SconaJPFC18, | ||
+ | author | ||
+ | | ||
+ | Yvan R. Petillot and | ||
+ | | ||
+ | | ||
+ | title = {StaticFusion: | ||
+ | | ||
+ | booktitle = {2018 {IEEE} International Conference on Robotics and Automation, {ICRA} | ||
+ | 2018, Brisbane, Australia, May 21-25, 2018}, | ||
+ | pages = {1--9}, | ||
+ | publisher = {{IEEE}}, | ||
+ | year = {2018}, | ||
+ | keywords = {vslam}, | ||
+ | } | ||
+ | |||
@article{Kukacka-et-al-2017, | @article{Kukacka-et-al-2017, | ||
Line 8247: | Line 8791: | ||
eprint = {1710.10686}, | eprint = {1710.10686}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {deep learning, neural networks, regularization, | + | keywords = {deep learning, neural networks, regularization, |
} | } | ||
@inproceedings{Golkov-et-al-ismrm2018-novelty, | @inproceedings{Golkov-et-al-ismrm2018-novelty, | ||
author = {V. Golkov and A. Vasilev and F. Pasa and I. Lipp and W. Boubaker and E. Sgarlata and F. Pfeiffer and V. Tomassini and D. K. Jones and D. Cremers}, | author = {V. Golkov and A. Vasilev and F. Pasa and I. Lipp and W. Boubaker and E. Sgarlata and F. Pfeiffer and V. Tomassini and D. K. Jones and D. Cremers}, | ||
- | title = {{q}-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis}, | + | title = {{q-Space} Novelty Detection in Short Diffusion |
year = {2018}, | year = {2018}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | ||
} | } | ||
Line 8262: | Line 8806: | ||
title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, | title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, | ||
year = {2019}, | year = {2019}, | ||
- | booktitle = {MICCAI 2019 International Workshop on Computational Diffusion MRI}, | + | booktitle = {{MICCAI} 2019 International Workshop on Computational Diffusion |
eprint = {1806.02997}, | eprint = {1806.02997}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI, deeplearning, | + | keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI}, |
award = {Oral Presentation} | award = {Oral Presentation} | ||
} | } | ||
Line 8271: | Line 8815: | ||
@inproceedings{Swazinna-et-al-ismrm2019, | @inproceedings{Swazinna-et-al-ismrm2019, | ||
author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | ||
- | title = {Negative-Unlabeled Learning for Diffusion MRI}, | + | title = {Negative-Unlabeled Learning for Diffusion |
year = {2019}, | year = {2019}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
- | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization, deeplearning}, | + | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization}, |
} | } | ||
@inproceedings{Golkov-et-al-ismrm2018-global, | @inproceedings{Golkov-et-al-ismrm2018-global, | ||
author = {V. Golkov and P. Swazinna and M. M. Schmitt and Q. A. Khan and C. M. W. Tax and M. Serahlazau and F. Pasa and F. Pfeiffer and G. J. Biessels and A. Leemans and D. Cremers}, | author = {V. Golkov and P. Swazinna and M. M. Schmitt and Q. A. Khan and C. M. W. Tax and M. Serahlazau and F. Pasa and F. Pfeiffer and G. J. Biessels and A. Leemans and D. Cremers}, | ||
- | title = {{q}-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization}, | + | title = {{q-Space} Deep Learning for {A}lzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization}, |
year = {2018}, | year = {2018}, | ||
- | booktitle = ismrm, | + | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, |
- | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, localization, deeplearning}, | + | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, localization}, |
} | } | ||
- | @inproceedings{Do-et-al-2018-pre-miRNA, | + | @article{Do-et-al-2018-pre-miRNA, |
author = {B. T. Do and V. Golkov and G. E. G\" | author = {B. T. Do and V. Golkov and G. E. G\" | ||
- | title = {Precursor microRNA Identification Using Deep Convolutional Neural Networks}, | + | title = {Precursor |
year = {2018}, | year = {2018}, | ||
% howpublished = {Preprint}, | % howpublished = {Preprint}, | ||
note = {{<a href=" | note = {{<a href=" | ||
- | | + | |
- | keywords = {precursor microRNA, pre-miRNA, miRNA, deep learning, neural networks, machine learning, deeplearning, biology, gene regulation}, | + | keywords = {precursor microRNA, pre-miRNA, miRNA, deep learning, neural networks, machine learning, biology, gene regulation}, |
+ | } | ||
+ | |||
+ | @article{Golkov-et-al-2020-ROC, | ||
+ | author = {V. Golkov and A. Becker and D. T. Plop and D. \v{C}uturilo and N. Davoudi and J. Mendenhall and R. Moretti and J. Meiler and D. Cremers}, | ||
+ | title = {Deep Learning for Virtual Screening: Five Reasons to Use {ROC} Cost Functions}, | ||
+ | year = {2020}, | ||
+ | journal = {arXiv preprint arXiv: | ||
+ | eprint = {2007.07029}, | ||
+ | eprinttype = {arXiv}, | ||
+ | keywords = {deep learning, drug discovery, virtual screening, neural networks, machine learning, QSAR, classification, | ||
} | } | ||
Line 8303: | Line 8857: | ||
eprint = {1801.07648}, | eprint = {1801.07648}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {deep learning, clustering, cluster analysis, neural networks, deeplearning}, | + | keywords = {deep learning, clustering, cluster analysis, neural networks}, |
} | } | ||
Line 8313: | Line 8867: | ||
month = " | month = " | ||
titleurl = {haeusser18associative.pdf}, | titleurl = {haeusser18associative.pdf}, | ||
- | keywords={Clustering, | + | keywords={Clustering, |
} | } | ||
- | + | @article{mayer18synthetic, | |
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | @InProceedings{mayer18synthetic, | + | |
author = {Nikolaus Mayer and Eddy Ilg and Philipp Fischer and Caner Hazirbas and Daniel Cremers and Alexey Dosovitskiy and Thomas Brox}, | author = {Nikolaus Mayer and Eddy Ilg and Philipp Fischer and Caner Hazirbas and Daniel Cremers and Alexey Dosovitskiy and Thomas Brox}, | ||
title = {What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? | title = {What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? | ||
booktitle= ijcv, | booktitle= ijcv, | ||
- | year = {2018}, | + | |
+ | number | ||
+ | pages = {1797--1812}, | ||
+ | year = {2019}, | ||
+ | year = {2018}, | ||
month = {September}, | month = {September}, | ||
eprint = {arXiv: | eprint = {arXiv: | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = {deeplearning, optical-flow} | + | keywords = {deep learning, optical-flow} |
} | } | ||
Line 8356: | Line 8907: | ||
booktitle={{IEEE/ | booktitle={{IEEE/ | ||
{IROS}}, | {IROS}}, | ||
- | year={2017} | + | year={2017}, |
+ | keywords = {vo, vio, vslam}, | ||
} | } | ||
Line 8403: | Line 8955: | ||
month = " | month = " | ||
note = {{<a href="/ | note = {{<a href="/ | ||
- | keywords={dso, | + | keywords={dso, |
} | } | ||
Line 8414: | Line 8966: | ||
month = " | month = " | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = tumvi | + | keywords = {tumvi, vo, vio ,vslam, dataset} |
} | } | ||
Line 8425: | Line 8977: | ||
arXiv = " | arXiv = " | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = vidsors | + | keywords = {vidsors, vo, vio ,vslam} |
} | } | ||
Line 8437: | Line 8989: | ||
month = " | month = " | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = {dso, ldso} | + | keywords = {dso, ldso, vslam} |
} | } | ||
Line 8456: | Line 9008: | ||
publisher = {IEEE}, | publisher = {IEEE}, | ||
note = {{<a href=" | note = {{<a href=" | ||
- | year = {2018} | + | year = {2018}, |
+ | keywords = {dso, vslam} | ||
} | } | ||
- | @InProceedings{eisenberger2019divfree, | + | @inproceedings{eisenberger2019divfree, |
author = "M. Eisenberger and Z. L\" | author = "M. Eisenberger and Z. L\" | ||
title = " | title = " | ||
booktitle = " | booktitle = " | ||
- | arXiv = " arXiv: | ||
year = " | year = " | ||
+ | volume={38}, | ||
+ | number={5}, | ||
+ | pages={1-12}, | ||
month = " | month = " | ||
note = {{<a href=" | note = {{<a href=" | ||
+ | keywords = { Geometry Processing }, | ||
} | } | ||
Line 8476: | Line 9032: | ||
month = " | month = " | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
- | note = {{<a href=" | + | note = {{<a href=" |
+ | keywords = { Geometry Processing }, | ||
} | } | ||
Line 8486: | Line 9043: | ||
month = " | month = " | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
- | note = {{<a href=" | + | note = {{<a href=" |
- | keywords = {dso, dvso, deep learning, deeplearning, monocular depth estimation, semi-supervised learning, slam, visual odometry} | + | keywords = {dso, dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry, vslam} |
} | } | ||
Line 8498: | Line 9055: | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
note = {{<a href="/ | note = {{<a href="/ | ||
+ | keywords = {dso, vslam} | ||
} | } | ||
Line 8510: | Line 9068: | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = double-sphere | + | keywords = {double-sphere, vslam} |
} | } | ||
Line 8523: | Line 9081: | ||
} | } | ||
+ | @article{laude2021lifting, | ||
+ | title = {Lifting the Convex Conjugate in Lagrangian Relaxations: | ||
+ | Approach for Continuous Markov Random Fields}, | ||
+ | author | ||
+ | Emanuel Laude and | ||
+ | Thomas M{\" | ||
+ | Michael M{\" | ||
+ | Daniel Cremers}, | ||
+ | journal | ||
+ | volume | ||
+ | number | ||
+ | pages = {1253--1281}, | ||
+ | year = {2022}, | ||
+ | keywords={Markov random fields, moment relaxation, sum of squares, polynomial optimization, | ||
+ | } | ||
@InProceedings{sundermeyer18implicit, | @InProceedings{sundermeyer18implicit, | ||
author = {M. Sundermeyer and Z. Marton and M. Durner and M. Brucker and R. Triebel}, | author = {M. Sundermeyer and Z. Marton and M. Durner and M. Brucker and R. Triebel}, | ||
Line 8569: | Line 9142: | ||
topic = "Shape Analysis", | topic = "Shape Analysis", | ||
award = {Received the Best Paper Award at 3DV 2018}, | award = {Received the Best Paper Award at 3DV 2018}, | ||
- | note = {{<a href=" | + | note = {{<a href=" |
+ | keywords = {Geometry Processing} | ||
} | } | ||
Line 8590: | Line 9164: | ||
} | } | ||
- | @article{tjaden2018region, | + | @article{tjaden2019region, |
title={A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking}, | title={A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking}, | ||
author={Tjaden, | author={Tjaden, | ||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, | journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, | ||
- | year={2018}, | + | |
+ | number | ||
+ | pages = {1797--1812}, | ||
+ | | ||
titleurl = {tjaden_et_al_pami18.pdf} | titleurl = {tjaden_et_al_pami18.pdf} | ||
} | } | ||
@inproceedings{wenzel18corl, | @inproceedings{wenzel18corl, | ||
- | author = {P. Wenzel and Q. Khan and D. Cremers and L. Leal-Taixé}, | + | author = {P. Wenzel and Q. Khan and D. Cremers and L. Leal-Taix\' |
- | booktitle = {Conference on Robot Learning (CoRL)}, | + | booktitle = {Conference on Robot Learning ({CoRL})}, |
- | title = {{Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs}}, | + | title = {Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using {GANs}}, |
year = {2018}, | year = {2018}, | ||
+ | | ||
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
- | @article{Frerix2019, | + | @InProceedings{Frerix2020, |
- | author = " | + | author = {Frerix, Thomas |
- | title = "Homogeneous Linear Inequality Constraints for Neural Network Activations", | + | title = {Homogeneous Linear Inequality Constraints for Neural Network Activations}, |
- | | + | |
- | | + | |
- | year = "2019", | + | note = {<a href="https:// |
- | | + | } |
+ | |||
+ | @inproceedings{Frerix2021, | ||
+ | title = {Variational Data Assimilation with a Learned Inverse Observation Operator}, | ||
+ | author = | ||
+ | booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML)}, | ||
+ | year = {2021}, | ||
+ | note = {<a href="http://proceedings.mlr.press/v139/frerix21a.html">URL</ | ||
} | } | ||
Line 8622: | Line 9207: | ||
address = " | address = " | ||
year = 2019, | year = 2019, | ||
- | | + | |
+ | keywords={vslam}, | ||
+ | | ||
} | } | ||
Line 8679: | Line 9266: | ||
doi={10.1109/ | doi={10.1109/ | ||
note = {{<a href=" | note = {{<a href=" | ||
- | keywords = nfr | + | keywords = {nfr, vo, vio, vslam} |
} | } | ||
@Article{Pasa-et-al-2019, | @Article{Pasa-et-al-2019, | ||
author={F. Pasa and V. Golkov and F. Pfeiffer and D. Cremers and D. Pfeiffer}, | author={F. Pasa and V. Golkov and F. Pfeiffer and D. Cremers and D. Pfeiffer}, | ||
- | title={Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization}, | + | title={Efficient Deep Network Architectures for Fast Chest {X}-Ray Tuberculosis Screening and Visualization}, |
journal={Scientific Reports}, | journal={Scientific Reports}, | ||
year={2019}, | year={2019}, | ||
Line 8693: | Line 9280: | ||
doi={10.1038/ | doi={10.1038/ | ||
url={https:// | url={https:// | ||
- | keywords={medical imaging,deeplearning} | + | keywords={medical imaging, |
} | } | ||
Line 8701: | Line 9288: | ||
booktitle={Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, | booktitle={Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, | ||
year={2020}, | year={2020}, | ||
- | note={{< | + | note={{< |
keywords={stereo, | keywords={stereo, | ||
} | } | ||
Line 8712: | Line 9299: | ||
eprint = {1905.03389}, | eprint = {1905.03389}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {evolutionary algorithms, evolutionary computation, | + | keywords = {evolutionary algorithms, evolutionary computation, |
} | } | ||
Line 8736: | Line 9323: | ||
@article{gn-net-2020, | @article{gn-net-2020, | ||
author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | ||
- | title = " | + | title = "{GN-Net}: The Gauss-Newton Loss for Multi-Weather Relocalization", |
- | journal = {IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA)}, | + | journal = {{IEEE} Robotics and Automation Letters ({RA-L})}, |
year = " | year = " | ||
volume={5}, | volume={5}, | ||
number={2}, | number={2}, | ||
pages={890-897}, | pages={890-897}, | ||
- | note = {{<a href=" | + | note = {{<a href=" |
- | keywords = {gn-net} | + | keywords = {gn-net, vslam, deep learning} |
+ | } | ||
+ | |||
+ | @article{sommer20planes, | ||
+ | author = {C. Sommer and Y. Sun and L. J. Guibas and D. Cremers and T. Birdal}, | ||
+ | title = "From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized | ||
+ | 3D Point Clouds", | ||
+ | journal = {IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA)}, | ||
+ | year = " | ||
+ | volume={5}, | ||
+ | number={2}, | ||
+ | pages={1764-1771}, | ||
+ | eprint = {2001.07360}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | doi = {10.1109/ | ||
+ | keywords = {Geometry Processing} | ||
} | } | ||
Line 8752: | Line 9355: | ||
year = " | year = " | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
- | note = {{<a href=" | + | note = {<a href=" |
+ | keywords = { Geometry Processing }, | ||
+ | } | ||
+ | |||
+ | @inproceedings{eisenberger2020hamiltonian, | ||
+ | author = "M. Eisenberger and D. Cremers", | ||
+ | title = " | ||
+ | booktitle = " | ||
+ | year = " | ||
+ | award = {Spotlight Presentation}, | ||
+ | note = {<a href=" | ||
+ | keywords = {Shape Analysis, Geometry Processing}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{eisenberger2020deepshells, | ||
+ | author = "M. Eisenberger and A. Toker and L. Leal{-}Taix{\' | ||
+ | title = "Deep Shells: Unsupervised Shape Correspondence with Optimal Transport", | ||
+ | booktitle = "34th Conference on Neural Information Processing Systems (NeurIPS)", | ||
+ | year = " | ||
+ | note = {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{eisenberger2021neuromorph, | ||
+ | author = "M. Eisenberger and D. Novotny and G. Kerchenbaum and P. Labatut and N. Neverova and D. Cremers and A. Vedaldi", | ||
+ | title = " | ||
+ | booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)", | ||
+ | year = " | ||
+ | note = {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{eisenberger2022unified, | ||
+ | author = "M. Eisenberger and A. Toker and L. Leal{-}Taix{\' | ||
+ | title = "A Unified Framework for Implicit Sinkhorn Differentiation", | ||
+ | booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)", | ||
+ | year = " | ||
+ | note = {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{eisenberger2023gmsm, | ||
+ | author = "M. Eisenberger and A. Toker and L. Leal{-}Taix{\' | ||
+ | title = " | ||
+ | booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)", | ||
+ | year = " | ||
+ | note = {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{mukkamala2021bregman, | ||
+ | address = {Cham}, | ||
+ | author = {M. C. Mukkamala and F. Westerkamp and E. Laude and D. Cremers and P. Ochs}, | ||
+ | booktitle = {Scale Space and Variational Methods in Computer Vision}, | ||
+ | date-modified = {2021-05-18 17:23:47 +0200}, | ||
+ | editor = {Elmoataz, Abderrahim and Fadili, Jalal and Qu{\' | ||
+ | isbn = {978-3-030-75549-2}, | ||
+ | pages = {204--215}, | ||
+ | publisher = {Springer International Publishing}, | ||
+ | title = {Bregman Proximal Gradient Algorithms for Deep Matrix Factorization}, | ||
+ | year = {2021}, | ||
+ | eprint = {1910.03638}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {math.OC}, | ||
} | } | ||
Line 8761: | Line 9423: | ||
year = " | year = " | ||
titleurl = {Weiss_et_al_cvpr2020.pdf}, | titleurl = {Weiss_et_al_cvpr2020.pdf}, | ||
+ | keywords = {Geometry Processing}, | ||
} | } | ||
@inproceedings{control-across-weathers-19, | @inproceedings{control-across-weathers-19, | ||
- | author = "Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixe", | + | author = {Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taix\' |
- | title = "Towards Generalizing Sensorimotor Control Across Weather Conditions", | + | title = {Towards Generalizing Sensorimotor Control Across Weather Conditions}, |
- | booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, | + | booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, |
- | year = "2019", | + | year = {2019}, |
- | note = {{<a href=" | + | note = {{<a href=" |
+ | | ||
} | } | ||
Line 8808: | Line 9472: | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
- | note = {{<a href=" | + | note = {{<a href=" |
+ | keywords = {Geometry Processing}, | ||
} | } | ||
@article{Della-Libera-et-al-2019, | @article{Della-Libera-et-al-2019, | ||
- | author = {L. Della Libera and V. Golkov | + | author = {Della Libera, L. and Golkov, |
- | title = {Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods}, | + | title = {Deep Learning for {2D and 3D} Rotatable Data: An Overview of Methods}, |
year = {2019}, | year = {2019}, | ||
journal = {arXiv preprint arXiv: | journal = {arXiv preprint arXiv: | ||
eprint = {1910.14594}, | eprint = {1910.14594}, | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
- | keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance, deeplearning}, | + | keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance}, |
} | } | ||
Line 8831: | Line 9496: | ||
doi = " | doi = " | ||
isbn = " | isbn = " | ||
+ | keywords = {vo, vio, vslam}, | ||
} | } | ||
Line 8844: | Line 9510: | ||
isbn=" | isbn=" | ||
doi=" | doi=" | ||
+ | keywords = {vo, vio, vslam}, | ||
} | } | ||
Line 8853: | Line 9520: | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
+ | doi = {10.1109/ | ||
booktitle=cvpr, | booktitle=cvpr, | ||
year = {2020}, | year = {2020}, | ||
- | award = {Oral Presentation} | + | award = {Oral Presentation}, |
+ | keywords={lie-spline, | ||
} | } | ||
Line 8877: | Line 9546: | ||
eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
award = {Oral Presentation}, | award = {Oral Presentation}, | ||
- | keywords = {dso, dvso, deep learning, deeplearning, monocular depth estimation, semi-supervised learning, slam, visual odometry} | + | keywords = {dso,dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry,d3vo, vslam} |
} | } | ||
+ | @InProceedings{fontan20information, | ||
+ | author | ||
+ | title = " | ||
+ | booktitle=cvpr, | ||
+ | year=2020, | ||
+ | award = {Oral Presentation}, | ||
+ | } | ||
+ | |||
+ | @InProceedings{sundermeyer20multi, | ||
+ | author | ||
+ | title = " | ||
+ | booktitle=cvpr, | ||
+ | year=2020, | ||
+ | } | ||
+ | |||
+ | @InProceedings{wenger20nonparametric, | ||
+ | author = "J. Wenger and H. Kjellstr\" | ||
+ | title = " | ||
+ | booktitle = {International Conference on Artificial | ||
+ | Intelligence and Statistics (AISTATS)}, | ||
+ | year = {2020}, | ||
+ | } | ||
+ | |||
+ | @InProceedings{lee20visual, | ||
+ | author = " J. Lee and R. Balachandran and Y. Sarkisov and M. De Stefano and A. Coelho and K. Shinde and M. J. Kim and R. Triebel and K. Kondak", | ||
+ | title = " | ||
+ | booktitle = icra, | ||
+ | year = {2020}, | ||
+ | } | ||
+ | |||
+ | @InProceedings{steidle19visual, | ||
+ | author = "F. Steidle and W. St\" | ||
+ | title = " | ||
+ | booktitle = " 11th International Micro Air Vehicle Competition and Conference (IMAV)", | ||
+ | year = {2019}, | ||
+ | keywords = {vslam} | ||
+ | } | ||
+ | | ||
+ | |||
+ | @InProceedings{feng19introspective, | ||
+ | author = "J. Feng and M. Durner and Z.-C. Marton and F. Balint-Benczedi and R. Triebel", | ||
+ | title = " | ||
+ | booktitle = " | ||
+ | year = {2019} | ||
+ | } | ||
+ | | ||
+ | | ||
+ | | ||
+ | @ARTICLE{giubilato20relocalization, | ||
+ | author = { R. Giubilato and M. Vayugundla and M. Schuster and W. St\" | ||
+ | title = {Relocalization With Submaps: Multi-Session Mapping for Planetary Rovers Equipped With Stereo Cameras}, | ||
+ | journal = "IEEE Robotics and Automation Letters", | ||
+ | volume = " | ||
+ | number = " | ||
+ | pages = " 580--587", | ||
+ | year = {2020}, | ||
+ | } | ||
+ | |||
+ | @ARTICLE{sundermeyer19augmented, | ||
+ | author={ M. Sundermeyer and Z. Marton and M. Durner and R. Triebel}, | ||
+ | title={Augmented Autoencoders: | ||
+ | journal=ijcv, | ||
+ | year={2019}, | ||
+ | } | ||
+ | |||
+ | @ARTICLE{lutz20ardea, | ||
+ | author={P. Lutz and M. G. M\" | ||
+ | title = {ARDEA—An MAV with skills for future planetary missions}, | ||
+ | journal={Journal of Field Robotics (JFR)}, | ||
+ | year={2020} | ||
+ | } | ||
+ | |||
+ | @inproceedings{ye2020optimization, | ||
+ | author = "Z. Ye and T. Möllenhoff and T. Wu and D. Cremers", | ||
+ | title = " | ||
+ | booktitle = {International Conference on Artificial | ||
+ | Intelligence and Statistics (AISTATS)}, | ||
+ | year = {2020}, | ||
+ | titleurl = {ye-et-al-combinatorial-20.pdf}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | }, | ||
+ | } | ||
+ | |||
+ | @inproceedings{ye2021gcpr, | ||
+ | author = "Z. Ye and B. Haefner and Y. Quéau and T. M\" | ||
+ | title = " | ||
+ | booktitle = {DAGM German Conference on Pattern Recognition (GCPR)}, | ||
+ | year = {2021}, | ||
+ | doi=" | ||
+ | %eprint = {}, | ||
+ | %eprinttype = {arXiv}, | ||
+ | %eprintclass = {cs.CV}, | ||
+ | award = {Oral Presentation}, | ||
+ | %titleurl = {}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | {<a href=" | ||
+ | }, | ||
+ | keywords = {}, | ||
+ | } | ||
+ | |||
+ | @article{ye2022ijcv, | ||
+ | author = "Z. Ye and B. Haefner and Y. Quéau and T. M\" | ||
+ | title = "A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces", | ||
+ | journal = {International Journal of Computer Vision (IJCV)}, | ||
+ | year = {2022}, | ||
+ | doi=" | ||
+ | titleurl = {ye2022ijcv.pdf}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | }, | ||
+ | keywords = {}, | ||
+ | } | ||
+ | |||
+ | @phdthesis{maier2020dissertation, | ||
+ | author={R. Maier}, | ||
+ | title={High-Quality {3D} Reconstruction from Low-Cost {RGB-D} Sensors}, | ||
+ | type={Dissertation}, | ||
+ | school={Technische Universit\" | ||
+ | address={M\" | ||
+ | year={2020} | ||
+ | } | ||
+ | |||
+ | @inbook{maier2020rgbdvision, | ||
+ | title={{RGB-D Vision}}, | ||
+ | author={R. Maier and D. Cremers}, | ||
+ | chapter={Encyclopedia of Robotics}, | ||
+ | editor={Ang, | ||
+ | year={2020}, | ||
+ | publisher={Springer Berlin Heidelberg}, | ||
+ | address={Berlin, | ||
+ | pages={1--11}, | ||
+ | isbn={978-3-642-41610-1}, | ||
+ | doi={10.1007/ | ||
+ | url={https:// | ||
+ | keywords = {rgbd, vslam} | ||
+ | } | ||
+ | |||
+ | @Inbook{Moeller2018, | ||
+ | author=" | ||
+ | editor=" | ||
+ | title=" | ||
+ | bookTitle=" | ||
+ | year=" | ||
+ | publisher=" | ||
+ | address=" | ||
+ | pages=" | ||
+ | titleurl = {moeller_cremers2020_denoising_old_and_new.pdf}, | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | @inproceedings{lee2020estimating, | ||
+ | author = " | ||
+ | title = " | ||
+ | booktitle = icml, | ||
+ | year = {2020} | ||
+ | } | ||
+ | |||
+ | @inproceedings{liu2020effective, | ||
+ | title={Effective Version Space Reduction for Convolutional Neural Networks}, | ||
+ | author={Liu, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2020}, | ||
+ | booktitle=ecml, | ||
+ | note={{< | ||
+ | keywords={deep learning, active learning, convolutional neural networks} | ||
+ | } | ||
+ | |||
+ | @inproceedings{denninger20scene, | ||
+ | title={{3D} Scene Reconstruction from a Single Viewport}, | ||
+ | author={Maximilian Denninger and Rudolph Triebel}, | ||
+ | year={2020}, | ||
+ | booktitle=eccv, | ||
+ | } | ||
+ | |||
+ | @inproceedings{du2020dh3d, | ||
+ | author = "J. Du and R. Wang and D. Cremers", | ||
+ | title = "DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization", | ||
+ | booktitle = " | ||
+ | year = " | ||
+ | award = {Spotlight Presentation}, | ||
+ | note={{< | ||
+ | keywords={SLAM, | ||
+ | } | ||
+ | |||
+ | @inproceedings{sewtz20robust, | ||
+ | title={Robust {MUSIC}-Based Sound Source Localization in Reverberant and Echoic Environments}, | ||
+ | author={Marco Sewtz and Tim Bodenm\" | ||
+ | year={2020}, | ||
+ | booktitle=iros, | ||
+ | note={to appear}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{legentil20gaussian, | ||
+ | title={Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments}, | ||
+ | author={Cedric {Le Gentil} and Mallikarjuna Vayugundla and Riccardo Giubilato and Wolfgang St\" | ||
+ | year={2020}, | ||
+ | booktitle=iros, | ||
+ | note={to appear}, | ||
+ | } | ||
+ | |||
+ | @InProceedings{sommer20primitect, | ||
+ | author={C. Sommer and Y. Sun and E. Bylow and D. Cremers}, | ||
+ | title={PrimiTect: | ||
+ | booktitle=icra, | ||
+ | eprint = {2005.07457}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | doi = {10.1109/ | ||
+ | year={2020}, | ||
+ | keywords = {Geometry Processing} | ||
+ | } | ||
+ | |||
+ | @InProceedings{koestler2020learning, | ||
+ | author = {L. Koestler and N. Yang and R. Wang and D. Cremers}, | ||
+ | title = {Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels}, | ||
+ | booktitle = {Proceedings of the German Conference on Pattern Recognition (GCPR)}, | ||
+ | year = {2020}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | @inproceedings{wenzel2020fourseasons, | ||
+ | title = {{4Seasons}: | ||
+ | author | ||
+ | booktitle = {Proceedings of the German Conference on Pattern Recognition ({GCPR})}, | ||
+ | year = {2020}, | ||
+ | note = {{<a href=" | ||
+ | keywords = {vslam, | ||
+ | } | ||
+ | |||
+ | |||
+ | @InProceedings{holzschuh20simanneal, | ||
+ | | ||
+ | title = {Simulated Annealing for 3D Shape Correspondence}, | ||
+ | | ||
+ | | ||
+ | award = {Oral Presentation}, | ||
+ | | ||
+ | } | ||
+ | |||
+ | @InProceedings{aygun20heatkernel, | ||
+ | | ||
+ | title = {Unsupervised Dense Shape Correspondence using Heat Kernels}, | ||
+ | | ||
+ | | ||
+ | | ||
+ | | ||
+ | } | ||
+ | |||
+ | @article{Naeyaert2020, | ||
+ | author = {Naeyaert, M. and Aelterman, J. and Van Audekerke, J. and Golkov, V. and Cremers, D. and Pižurica, A. and Sijbers, J. and Verhoye, M.}, | ||
+ | title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing}, | ||
+ | journal = {Magnetic Resonance in Medicine}, | ||
+ | year = {2020}, | ||
+ | volume = {85}, | ||
+ | number = {3}, | ||
+ | pages = {1397-1413}, | ||
+ | keywords = {compressed sensing, diffusion, fast spin echo, HARDI, turbo spin echo, medical imaging, diffusion MRI}, | ||
+ | doi = {10.1002/ | ||
+ | url = {https:// | ||
+ | eprint = {https:// | ||
+ | } | ||
+ | @InProceedings{boerdijk2020self, | ||
+ | author | ||
+ | title = {Self-Supervised Object-in-Gripper Segmentation from Robotic Motions}, | ||
+ | booktitle | ||
+ | year = {2020}, | ||
+ | note = {} | ||
+ | } | ||
+ | @InProceedings{schiel2020incremental, | ||
+ | author | ||
+ | title = {Incremental learning of EMG-based control commands using Gaussian Processes}, | ||
+ | booktitle | ||
+ | note = {}, | ||
+ | year = {2020} | ||
+ | } | ||
+ | @InProceedings{stoiber2020sparse, | ||
+ | author | ||
+ | title = {A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking}, | ||
+ | booktitle | ||
+ | year = {2020}, | ||
+ | note = {}, | ||
+ | award = {Best Paper Award} | ||
+ | } | ||
+ | @InProceedings{meyer2020robust, | ||
+ | author | ||
+ | title = {Robust Vision-Based Pose Correction for a Robotic Manipulator using Active Markers}, | ||
+ | booktitle | ||
+ | year = {2020}, | ||
+ | note = {to appear} | ||
+ | } | ||
+ | |||
+ | @inproceedings{demmel2020distributed, | ||
+ | | ||
+ | title = {Distributed Photometric Bundle Adjustment}, | ||
+ | | ||
+ | | ||
+ | award = {Oral Presentation}, | ||
+ | | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @article{fabbro2020, | ||
+ | title={Speech Synthesis and Control Using Differentiable {DSP}}, | ||
+ | author={Giorgio Fabbro and Vladimir Golkov and Thomas Kemp and Daniel Cremers}, | ||
+ | year={2020}, | ||
+ | journal = {arXiv preprint arXiv: | ||
+ | eprint={2010.15084}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={eess.AS}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | }, | ||
+ | keywords={speech synthesis, neural vocoder, text-to-speech, | ||
+ | } | ||
+ | |||
+ | @inproceedings{lm-reloc-2020, | ||
+ | | ||
+ | title = {LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization}, | ||
+ | | ||
+ | year = {2020}, | ||
+ | | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{wimbauer2020monorec, | ||
+ | title={MonoRec: | ||
+ | author={F. Wimbauer and N. Yang and L. von Stumberg and N. Zeller and Daniel Cremers}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2021}, | ||
+ | eprint = {2011.11814}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | keywords={monorec, | ||
+ | note={{< | ||
+ | } | ||
+ | |||
+ | @InProceedings{yenamandra2020i3dmm, | ||
+ | author = {Tarun Yenamandra and Ayush Tewari and Florian Bernard and Hans-Peter Seidel and Mohamed Elgharib and Daniel Cremers and Christian Theobalt}, | ||
+ | title = {i3DMM: Deep Implicit 3D Morphable Model of Human Heads}, | ||
+ | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
+ | month = {June}, | ||
+ | year = {2021}, | ||
+ | keywords = {Geometry Processing, implicit representations, | ||
+ | award = {Oral Presentation}, | ||
+ | note={{< | ||
+ | } | ||
+ | |||
+ | @article{chiotellis2020noge, | ||
+ | title={Neural Online Graph Exploration}, | ||
+ | author={Ioannis Chiotellis and Daniel Cremers}, | ||
+ | year={2020}, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | eprint={2012.03345}, | ||
+ | archivePrefix={arXiv}, | ||
+ | primaryClass={cs.LG}, | ||
+ | keywords={deep learning, exploration, | ||
+ | note={{< | ||
+ | } | ||
+ | |||
+ | @inproceedings{gao2021multi, | ||
+ | title={Isometric Multi-Shape Matching}, | ||
+ | author={Maolin Gao and Zorah L\" | ||
+ | year={2021}, | ||
+ | booktitle=cvpr, | ||
+ | keywords={Shape Analysis, Geometry Processing, Shape Correspondence, | ||
+ | award = {Oral Presentation}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @article{mueller2021, | ||
+ | title={Rotation-Equivariant Deep Learning for Diffusion {MRI}}, | ||
+ | author={P. M\" | ||
+ | year={2021}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2102.06942}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | keywords={deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, | ||
+ | } | ||
+ | |||
+ | @inproceedings{Naeyaert2021, | ||
+ | author = {Maarten Naeyaert and Vladimir Golkov and Daniel Cremers and Jan Sijbers and Marleen Verhoye}, | ||
+ | title = {Faster and better {HARDI} using {FSE} and holistic reconstruction}, | ||
+ | year = {2021}, | ||
+ | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, | ||
+ | keywords = {compressed sensing, magnetic resonance imaging, diffusion MRI, fast spin echo, HARDI, turbo spin echo, primal-dual, | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{Mueller2021-ISMRM, | ||
+ | title = {Rotation-Equivariant Deep Learning for Diffusion {MRI} (short version)}, | ||
+ | | ||
+ | year = {2021}, | ||
+ | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, | ||
+ | | ||
+ | } | ||
+ | |||
+ | @inproceedings{selfcontrol-aistats-2021, | ||
+ | | ||
+ | title = {Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry}, | ||
+ | | ||
+ | | ||
+ | year = {2021}, | ||
+ | | ||
+ | } | ||
+ | |||
+ | @InProceedings{gladkova2021tight, | ||
+ | author={Mariia Gladkova and Rui Wang and Niclas Zeller and Daniel Cremers}, | ||
+ | title={Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry}, | ||
+ | booktitle={Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, | ||
+ | year={2021}, | ||
+ | eprint = {2102.01191}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note={{< | ||
+ | keywords={relocalization, | ||
+ | } | ||
+ | |||
+ | @InProceedings{yan2021soe, | ||
+ | author = {Y. Xia and Y. Xu and S. Li and R. Wang and J. Du and D. Cremers and U. Stilla}, | ||
+ | title = {SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2021}, | ||
+ | award = {Oral Presentation}, | ||
+ | note = {{<a href=" | ||
+ | keywords={SLAM, | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{wenzel2021icra, | ||
+ | | ||
+ | title = {Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning}, | ||
+ | | ||
+ | | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{sewtz2021icara, | ||
+ | | ||
+ | title = {Robust Approaches for Localization on Multi-camera Systems in Dynamic Environments}, | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{winkelbauer2021icra, | ||
+ | | ||
+ | title = {Learning to Localize in New Environments from Synthetic Training Data}, | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{lehner2021icra, | ||
+ | | ||
+ | title = {Exploration of Large Outdoor Environments Using Multi-Criteria Decision Making}, | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{boerdijk2021icra, | ||
+ | | ||
+ | title = {" | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{sundermeyer2021icra, | ||
+ | | ||
+ | title = {{Contact-GraspNet}: | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{ballester2021icra, | ||
+ | | ||
+ | title = {DOT: Dynamic Object Tracking for Visual SLAM}, | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | @inproceedings{ballester2021icra, | ||
+ | | ||
+ | title = {DOT: Dynamic Object Tracking for Visual SLAM}, | ||
+ | | ||
+ | year = {2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{demmel2021rootba, | ||
+ | author = {Nikolaus Demmel and Christiane Sommer and Daniel Cremers and Vladyslav Usenko}, | ||
+ | title = {Square Root Bundle Adjustment for Large-Scale Reconstruction}, | ||
+ | eprint = {2103.01843}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2021}, | ||
+ | keywords = {bundle adjustment, structure from motion, optimization, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{tomani2021falcon, | ||
+ | author = " | ||
+ | title = " | ||
+ | booktitle = In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-2021), | ||
+ | year = " | ||
+ | eprint={2012.10923}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords={deep learning} | ||
+ | } | ||
+ | |||
+ | @inproceedings{tomani2021posthoc, | ||
+ | author = " | ||
+ | title = " | ||
+ | booktitle = cvpr, | ||
+ | year = " | ||
+ | award = "Oral Presentation", | ||
+ | eprint={2012.10988}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords={deep learning} | ||
+ | } | ||
+ | |||
+ | @inproceedings{tomani2021pts, | ||
+ | title={Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration}, | ||
+ | author={Christian Tomani and Daniel Cremers and Florian Buettner}, | ||
+ | year={2021}, | ||
+ | booktitle = eccv, | ||
+ | year = " | ||
+ | eprint={2102.12182}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords={deep learning}, | ||
+ | } | ||
+ | |||
+ | @article{tomani2022challenger, | ||
+ | title={Challenger: | ||
+ | author={Christian Tomani and Daniel Cremers}, | ||
+ | year={2022}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2205.15094}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.LG}, | ||
+ | keywords={deep learning}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{tomani2023dac, | ||
+ | title = {Beyond In-Domain Scenarios: Robust Density-Aware Calibration}, | ||
+ | author = {Tomani, Christian and Waseda, Futa and Shen, Yuesong and Cremers, Daniel}, | ||
+ | booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)}, | ||
+ | year = {2023}, | ||
+ | eprint={2302.05118}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords={deep learning}, | ||
+ | } | ||
+ | |||
+ | @article{tomani2023qualityaware, | ||
+ | title={Quality Control at Your Fingertips: Quality-Aware Translation Models}, | ||
+ | author={Christian Tomani and David Vilar and Markus Freitag and Colin Cherry and Subhajit Naskar and Mara Finkelstein and Daniel Cremers}, | ||
+ | year={2023}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2310.06707}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.LG}, | ||
+ | keywords={deep learning}, | ||
+ | } | ||
+ | |||
+ | @INPROCEEDINGS{Publ2015-866, | ||
+ | | ||
+ | | ||
+ | | ||
+ | | ||
+ | | ||
+ | | ||
+ | } | ||
+ | |||
+ | @InProceedings{lyssenko21from, | ||
+ | author | ||
+ | title = {From Evaluation to Verification: | ||
+ | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, | ||
+ | month = {June}, | ||
+ | year = {2021}, | ||
+ | pages = {38-45} | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{demmel2021rootvo, | ||
+ | author = {Nikolaus Demmel and David Schubert and Christiane Sommer and Daniel Cremers and Vladyslav Usenko}, | ||
+ | title = {Square Root Marginalization for Sliding-Window Bundle Adjustment}, | ||
+ | eprint = {2109.02182}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle=iccv, | ||
+ | year = {2021}, | ||
+ | keywords = {odometry, VO, VIO, visual-inertial, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{wudenka2021monocular, | ||
+ | | ||
+ | title = {Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions}, | ||
+ | eprint = {2109.05509}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.RO}, | ||
+ | | ||
+ | keywords = {odometry, VO, SLAM, vslam}, | ||
+ | year = {2021}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{klenk2021tumvie, | ||
+ | author = {Simon Klenk and Jason Chui and Nikolaus Demmel and Daniel Cremers}, | ||
+ | title = {TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset}, | ||
+ | eprint = {2108.07329}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle=iros, | ||
+ | year = {2021}, | ||
+ | keywords = {tumvie, event camera, dynamic vision sensor, SLAM, vslam}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @article{chui2021etslo, | ||
+ | title={Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization}, | ||
+ | author={J. Chui and S. Klenk and D. Cremers}, | ||
+ | year={2021}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2107.04536}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | keywords={Dynamic vision sensor, Continuous-time feature tracking, Sliding window, B-splines, SE2 warping, SLAM, vslam}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{koestler2021tandem, | ||
+ | author = {Lukas Koestler and Nan Yang and Niclas Zeller and Daniel Cremers}, | ||
+ | title = {TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo}, | ||
+ | booktitle=corl, | ||
+ | year = {2021}, | ||
+ | eprint={2111.07418}, | ||
+ | eprinttype={arXiv}, | ||
+ | award = {3DV' | ||
+ | keywords = {tandem, odometry, VO, SLAM, vslam, dense reconstruction, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2021mcg, | ||
+ | author = {Simon Weber and Nikolaus Demmel and Daniel Cremers}, | ||
+ | title = {Multidirectional Conjugate Gradients for Scalable Bundle Adjustment}, | ||
+ | booktitle=gcpr, | ||
+ | year = {2021}, | ||
+ | keywords = {large-scale reconstruction, | ||
+ | award = {Oral Presentation}, | ||
+ | note = {<a href=" | ||
+ | } | ||
+ | |||
+ | @article{mozes2021, | ||
+ | title={Scene Graph Generation for Better Image Captioning? | ||
+ | author={M. Mozes and M. Schmitt and V. Golkov and H. Sch\" | ||
+ | year={2021}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2109.11398}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | keywords={deep learning, computer vision, natural language processing, image captioning, scene graphs, attention mechanism}, | ||
+ | } | ||
+ | |||
+ | @phdthesis{Golkov-PhDthesis, | ||
+ | author = {V. Golkov}, | ||
+ | title = {Deep learning and variational analysis for high-dimensional and geometric biomedical data}, | ||
+ | school = {Department of Informatics, | ||
+ | Germany}, | ||
+ | year = {2021}, | ||
+ | topic = {deep learning, medical imaging, protein structure prediction, neural networks, machine learning, calculus of variations, diffusion MRI, magnetic resonance imaging}, | ||
+ | keywords = {deep learning, medical imaging, protein structure prediction, neural networks, machine learning, calculus of variations, diffusion MRI, magnetic resonance imaging}, | ||
+ | } | ||
+ | |||
+ | @article{stumberg22dmvio, | ||
+ | author = "L. von Stumberg and D. Cremers", | ||
+ | title = " | ||
+ | journal = {{IEEE} Robotics and Automation Letters ({RA-L}) & International Conference on Robotics and Automation ({ICRA})}, | ||
+ | year = " | ||
+ | volume={7}, | ||
+ | number={2}, | ||
+ | pages={1408-1415}, | ||
+ | doi={10.1109/ | ||
+ | note = {{<a href=" | ||
+ | keywords = {dm-vio, dso, vslam, SLAM, VIO, visual-inertial, | ||
+ | } | ||
+ | @inproceedings{lee21trust, | ||
+ | | ||
+ | month = {November}, | ||
+ | title = {Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse {G}aussian Processes}, | ||
+ | | ||
+ | author = {Jongseok Lee and Jianxiang Feng and Matthias Humt and Marcus Gerhard M{\" | ||
+ | year = {2021}, | ||
+ | } | ||
+ | @inproceedings{schnaus21bayesian, | ||
+ | author = {Dominik Schnaus and Jongseok Lee and Rudolph Triebel}, | ||
+ | | ||
+ | year = {2021}, | ||
+ | title = {Kronecker-Factored Optimal Curvature}, | ||
+ | | ||
+ | note = {{<a href="/ | ||
+ | } | ||
+ | @inproceedings{liao21learning, | ||
+ | | ||
+ | author = {Hsuan-Cheng Liao and Riccardo Giubilato and Wolfgang St{\" | ||
+ | year = {2021}, | ||
+ | title = {Learning-Based Matching of {3D} Submaps from Dense Stereo for Planetary-Like Environments}, | ||
+ | } | ||
+ | @inproceedings{giubilato21multi, | ||
+ | | ||
+ | author = {Riccardo Giubilato and Mallikarjuna Vayugundla and Wolfgang St{\" | ||
+ | | ||
+ | title = {Multi-Modal Loop Closing in Unstructured Planetary Environments with Visually Enriched Submaps}, | ||
+ | year = {2021}, | ||
+ | } | ||
+ | @inproceedings{durner21unknown, | ||
+ | title = {Unknown Object Segmentation from Stereo Images}, | ||
+ | year = {2021}, | ||
+ | author = {Maximilian Durner and Wout Boerdijk and Martin Sundermeyer and Werner Friedl and Zoltan-Csaba Marton and Rudolph Triebel}, | ||
+ | | ||
+ | } | ||
+ | |||
+ | @inproceedings{lyssenko21instance, | ||
+ | author = {Maria Lyssenko and Christoph Gladisch and Christian Heinzemann and Matthias Woehrle and Rudolph Triebel}, | ||
+ | | ||
+ | title = {Instance Segmentation in {CARLA}: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes}, | ||
+ | pages = {988--996}, | ||
+ | year = {2021}, | ||
+ | | ||
+ | } | ||
+ | @inproceedings{mueller21photo, | ||
+ | title = {A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments}, | ||
+ | year = {2021}, | ||
+ | author = {Marcus Gerhard M{\" | ||
+ | | ||
+ | } | ||
+ | |||
+ | @inproceedings{Veraart2022, | ||
+ | title = {A data-driven variability assessment of brain diffusion {MRI} preprocessing pipelines}, | ||
+ | | ||
+ | year = {2022}, | ||
+ | booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, | ||
+ | | ||
+ | award = {Oral Presentation} | ||
+ | } | ||
+ | |||
+ | @inproceedings{Sommer2022, | ||
+ | author | ||
+ | Lu Sang and | ||
+ | David Schubert and | ||
+ | | ||
+ | title = {Gradient-{SDF}: | ||
+ | booktitle= cvpr, | ||
+ | year = {2022}, | ||
+ | url = {https:// | ||
+ | titleurl = {sommer2022.png}, | ||
+ | note = { | ||
+ | {<a href="/ | ||
+ | {<a href="/ | ||
+ | {<a href=" | ||
+ | } | ||
+ | } | ||
+ | |||
+ | @inproceedings{ye2021joint, | ||
+ | title = {Joint Deep Multi-Graph Matching and {3D} Geometry Learning from Inhomogeneous {2D} Image Collections}, | ||
+ | author = {Zhenzhang Ye and Tarun Yenamandra and Florian Bernard and Daniel Cremers}, | ||
+ | booktitle = AAAI, | ||
+ | titleurl = {ye2021joint.pdf}, | ||
+ | note = { {<a href="/ | ||
+ | year = {2022}, | ||
+ | |||
+ | } | ||
+ | |||
+ | @article{Brunner2022, | ||
+ | author | ||
+ | title = {Deep Learning in Attosecond Metrology}, | ||
+ | journal | ||
+ | year = {2022}, | ||
+ | keywords = {attosecond metrology, photoelectron spectroscopy, | ||
+ | volume | ||
+ | number | ||
+ | pages = {15669--15684}, | ||
+ | publisher = {OSA}, | ||
+ | url = {https:// | ||
+ | doi = {10.1364/ | ||
+ | award = {Editor' | ||
+ | } | ||
+ | |||
+ | @InProceedings{yenamandra2024fire, | ||
+ | author | ||
+ | title = {FIRe: Fast Inverse Rendering Using Directional and Signed Distance Functions}, | ||
+ | booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, | ||
+ | month = {January}, | ||
+ | year = {2024}, | ||
+ | pages = {3077-3087} | ||
+ | } | ||
+ | |||
+ | @inproceedings{muhle2022pnec, | ||
+ | author = {Dominik Muhle and Lukas Koestler and Nikolaus Demmel and Florian Bernard and Daniel Cremers}, | ||
+ | title = {The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions}, | ||
+ | eprint = {2204.02256}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2022}, | ||
+ | keywords = {pnec, vo, vslam}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{wimbauer2022rendering, | ||
+ | title={De-rendering 3D Objects in the Wild}, | ||
+ | author={Wimbauer, | ||
+ | booktitle=cvpr, | ||
+ | year={2022}, | ||
+ | eprint={2201.02279}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{pccontrol_2022, | ||
+ | title = {Lateral Ego-Vehicle Control Without Supervision Using Point Clouds}, | ||
+ | author = {Florian M{\" | ||
+ | booktitle = {Pattern Recognition and Artificial Intelligence}, | ||
+ | publisher= {Springer International Publishing}, | ||
+ | keywords = {deep learning}, | ||
+ | pages={477--488}, | ||
+ | isbn={978-3-031-09037-0}, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{pathfinding_2022, | ||
+ | title = {Biologically Inspired Neural Path Finding}, | ||
+ | author = {Li Hang and Qadeer Khan and Volker Tresp and Daniel Cremers}, | ||
+ | booktitle = {Brain Informatics}, | ||
+ | publisher= {Springer International Publishing}, | ||
+ | keywords = {deep learning}, | ||
+ | note = {{<a href=" | ||
+ | year = {2022}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{ventriloquist_2022, | ||
+ | title = {Ventriloquist-Net: | ||
+ | author = {Deepan Das and Qadeer Khan and Daniel Cremers}, | ||
+ | booktitle = {IEEE International Conference on Image Processing}, | ||
+ | keywords = {deep learning}, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{koestler2022intrinsic, | ||
+ | author = {Lukas Koestler and Daniel Grittner and Michael Moeller and Daniel Cremers and Zorah L{\" | ||
+ | title = {Intrinsic Neural Fields: Learning Functions on Manifolds}, | ||
+ | booktitle=ECCV, | ||
+ | year = {2022}, | ||
+ | eprint={2203.07967}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords = {Neural Fields, Shape Analysis, Geometry Processing}, | ||
+ | note = {{Code will be released soon.}}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{fontan2022model, | ||
+ | author = {Alejandro Fontán Villacampa and Laura Oliva Maza and Javier Civera and Rudolph Triebel}, | ||
+ | title = {A Model for Multi-View Residual Covariances Based on Perspective Deformation}, | ||
+ | booktitle=icra, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{aljabout2022seeking, | ||
+ | author = {Elie Aljalbout and Maximilian Ulmer and Rudolph Triebel}, | ||
+ | title = {Seeking Visual Discomfort: Curiosity-Driven Representations for Reinforcement Learning}, | ||
+ | booktitle=icra, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{stoiber2022iterative, | ||
+ | author = {Manuel Stoiber and Martin Sundermeyer and Rudolph Triebel}, | ||
+ | title = {Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{boerdijk2022towards, | ||
+ | author = {Wout Boerdijk and Maximilian Durner and Martin Sundermeyer and Rudolph Triebel}, | ||
+ | title = {Towards Robust Perception of Unknown Objects in the Wild}, | ||
+ | booktitle={ICRA Workshop on Robotic Perception and Mapping: Emerging Techniques}, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{lyssenko22towards, | ||
+ | author = {Maria Lyssenko and Christoph David Gladisch and Christian Heinzemann and Matthias Woehrle and Rudolph Triebel}, | ||
+ | title = {Towards Safety-Aware Pedestrian Detection in Autonomous Systems}, | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{knauer22recall, | ||
+ | author = {Markus Knauer and Maximilian Denninger and Rudolph Triebel}, | ||
+ | title = {RECALL: Rehearsal-free Continual Learning for Object Classification}, | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{winkelbauer22two, | ||
+ | author = {Dominik Winkelbauer and Berthold Bäuml and Nils Thuerey and Rudolph Triebel}, | ||
+ | title = {A Two-stage Learning Architecture that Generates High-Quality Grasps for a Multi-Fingered Hand}, | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{feng22bayesian, | ||
+ | author = {Jianxiang Feng and Jongseok Lee and Maximilian Durner and Rudolph Triebel}, | ||
+ | title = {Bayesian Active Learning for Sim-to-Real Robotic Perception}, | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{meyer22probabilistic, | ||
+ | author = {Lukas Meyer and Klaus H. Strobl and Rudolph Triebel}, | ||
+ | title = {The Probabilistic Robot Kinematics Model and its Application to Sensor Fusion}, | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | @inproceedings{giubilato22challenges, | ||
+ | author = {Riccardo Giubilato and Wolfgang Stürzl and Armin Wedler and Rudolph Triebel}, | ||
+ | title = {Challenges of SLAM in extremely unstructured environments: | ||
+ | booktitle=iros, | ||
+ | year = {2022}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{gladkova2022directtracker, | ||
+ | author = {Mariia Gladkova and Nikita Korobov and Nikolaus Demmel and Aljoša Ošep and Laura Leal-Taixé and Daniel Cremers}, | ||
+ | title = {DirectTracker: | ||
+ | booktitle = {iros}, | ||
+ | year = {2022}, | ||
+ | eprint = {2209.14965}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note={{< | ||
+ | keywords = {multi-object tracking, 3D object detection, slam, scene understanding} | ||
+ | } | ||
+ | |||
+ | @inproceedings{hofherr2023neuralPhysParam, | ||
+ | author = {Florian Hofherr and Lukas Koestler and Florian Bernard and Daniel Cremers}, | ||
+ | title = {Neural Implicit Representations for Physical Parameter Inference from a Single Video}, | ||
+ | booktitle=wacv, | ||
+ | year = {2023}, | ||
+ | eprint={2204.14030}, | ||
+ | eprinttype={arXiv}, | ||
+ | keywords = {Neural Fields, Physical Parameter Estimation, Geometry Processing}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{wang2021epfgnn, | ||
+ | author | ||
+ | | ||
+ | | ||
+ | title = {Explicit pairwise factorized graph neural network for semi-supervised | ||
+ | node classification}, | ||
+ | booktitle = {UAI}, | ||
+ | year = {2021}, | ||
+ | eprint={2107.13059}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.LG}, | ||
+ | keywords = {deep learning, graph neural network}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{hsu2022gats, | ||
+ | title={What Makes Graph Neural Networks Miscalibrated? | ||
+ | author={Hans Hao-Hsun Hsu and Yuesong Shen and Christian Tomani and Daniel Cremers}, | ||
+ | booktitle = {NeurIPS}, | ||
+ | year = {2022}, | ||
+ | eprint={2210.06391}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.LG}, | ||
+ | keywords = {deep learning, graph neural network, calibration}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{shen2022dca, | ||
+ | title={Deep Combinatorial Aggregation}, | ||
+ | author={Yuesong Shen and Daniel Cremers}, | ||
+ | booktitle = {NeurIPS}, | ||
+ | year = {2022}, | ||
+ | eprint={2210.06436}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.LG}, | ||
+ | keywords = {deep learning, uncertainty-aware learning}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{sang2023high, | ||
+ | author = {Sang, Lu and Haefner, Bjoern and Zuo, Xingxing and Cremers, Daniel}, | ||
+ | title = {High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF}, | ||
+ | booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)}, | ||
+ | month={January}, | ||
+ | address={Hawaii, | ||
+ | year = {2023}, | ||
+ | eprint = {2210.12202}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}, | ||
+ | titleurl = {sang2023high.png}, | ||
+ | award = {Spotlight Presentation}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | } | ||
+ | }, | ||
+ | keywords = {3d-reconstruction, | ||
+ | } | ||
+ | |||
+ | @inproceedings{hsu2022a, | ||
+ | title={A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs}, | ||
+ | author={Hans Hao-Hsun Hsu and Yuesong Shen and Daniel Cremers}, | ||
+ | booktitle={NeurIPS 2022 Workshop: New Frontiers in Graph Learning}, | ||
+ | year={2022}, | ||
+ | eprint={2210.15575}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.LG}, | ||
+ | keywords = {deep learning, graph neural network, uncertainty estimation, calibration}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | |||
+ | @article{saroha2022implicit, | ||
+ | title={Implicit Shape Completion via Adversarial Shape Priors}, | ||
+ | author={Saroha, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2022}, | ||
+ | titleurl = {saroha2022implicit.png} | ||
+ | |||
+ | } | ||
+ | |||
+ | @inproceedings{ehm2021shortest, | ||
+ | title={Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis}, | ||
+ | author={Ehm, | ||
+ | booktitle={2021 International Conference on 3D Vision (3DV)}, | ||
+ | pages={1186--1195}, | ||
+ | year={2021}, | ||
+ | titleurl = {ehm2021_mvsp.png}, | ||
+ | organization={IEEE} | ||
+ | } | ||
+ | |||
+ | @article{klenk2023nerf, | ||
+ | title={E-nerf: | ||
+ | author={Klenk, | ||
+ | journal={IEEE Robotics and Automation Letters}, | ||
+ | volume={8}, | ||
+ | number={3}, | ||
+ | pages={1587--1594}, | ||
+ | year={2023}, | ||
+ | publisher={IEEE}, | ||
+ | keywords = {event camera, enerf, nerf}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{klenk2022masked, | ||
+ | title={Masked Event Modeling: Self-Supervised Pretraining for Event Cameras}, | ||
+ | author={Klenk, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2022}, | ||
+ | eprint = {2212.10368}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle={{IEEE Winter Conference on Applications of Computer Vision (WACV)}}, | ||
+ | month={January}, | ||
+ | address={Hawaii, | ||
+ | year = {2024}, | ||
+ | } | ||
+ | |||
+ | @article{wenzel2022seasons, | ||
+ | title={4Seasons: | ||
+ | author={Wenzel, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2022}, | ||
+ | eprint = {2301.01147}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV} | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | @article{nndriving2023, | ||
+ | title={Learning vision based autonomous lateral vehicle control without supervision}, | ||
+ | author={Khan, | ||
+ | journal={Applied Intelligence}, | ||
+ | pages={1--13}, | ||
+ | year={2023}, | ||
+ | publisher={Springer}, | ||
+ | | ||
+ | note = {{<a href=" | ||
+ | |||
+ | keywords = {intelligent driving, neural networks, deep learning}, | ||
+ | } | ||
+ | |||
+ | @InProceedings{Dekel_2020_CVPR, | ||
+ | author = {Dekel, Amit and Härenstam-Nielsen, | ||
+ | title = {Optimal least-squares solution to the hand-eye calibration problem}, | ||
+ | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern | ||
+ | Recognition (CVPR)}, | ||
+ | month = {June}, | ||
+ | year = {2020}, | ||
+ | titleurl = {linus_handeye.png}, | ||
+ | eprint={2002.10838}, | ||
+ | eprinttype={arXiv}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{sdrRobustTriangulation, | ||
+ | author = {Härenstam-Nielsen, | ||
+ | title = {Semidefinite Relaxations for Robust Multiview Triangulation}, | ||
+ | booktitle=cvpr, | ||
+ | year = {2023}, | ||
+ | titleurl = {robust_triangulation.png}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprint={2301.11431}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2023power, | ||
+ | title = {Power Bundle Adjustment for Large-Scale 3D Reconstruction}, | ||
+ | author = {Weber, Simon and Demmel, Nikolaus and Chon Chan, Tin and Cremers, Daniel}, | ||
+ | eprint = {2204.12834}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle= cvpr, | ||
+ | keywords = {bundle adjustment, optimization, | ||
+ | note={<a href=" | ||
+ | titleurl = {weber2022psc.pdf}, | ||
+ | year = {2023} | ||
+ | } | ||
+ | |||
+ | @InProceedings{wimbauer2023behind, | ||
+ | title={Behind the Scenes: Density Fields for Single View Reconstruction}, | ||
+ | author={Wimbauer, | ||
+ | booktitle=cvpr, | ||
+ | year = {2023}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprint={2301.07668}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | keywords={depth prediction, volumetric, nerf, mvs, deep learning, SLAM, vslam, reconstruction}, | ||
+ | note={{< | ||
+ | titleurl={wimbauer2023behind.png} | ||
+ | } | ||
+ | |||
+ | @InProceedings{meier2023niff, | ||
+ | title={NIFF: | ||
+ | via Neural Instance Feature Forging}, | ||
+ | author={Guirguis, | ||
+ | booktitle=cvpr, | ||
+ | year = {2023}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprint={2303.04958}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | keywords={omputer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, | ||
+ | titleurl={meier2023niff.png} | ||
+ | } | ||
+ | |||
+ | @InProceedings{meier2021boltzman, | ||
+ | author = {Borisov, Vadim and Meier, Johannes and Heuvel, Johan van den and Jalali, Hamed and Kasneci, Gjergji}, | ||
+ | title = {A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines}, | ||
+ | year = {2021}, | ||
+ | booktitle = nipsd, | ||
+ | eprinttype={arXiv}, | ||
+ | eprint={2111.07379}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences} | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | @InProceedings{Limitless, | ||
+ | author = "Koke, Christian", | ||
+ | title = " | ||
+ | booktitle = " | ||
+ | primaryClass = " | ||
+ | eprint = {2301.11443}, | ||
+ | eprinttype = {arXiv}, | ||
+ | year = " | ||
+ | } | ||
+ | |||
+ | @InProceedings{GraphWaveletBeyond, | ||
+ | author = "Koke, Christian and Kutyniok, Gitta", | ||
+ | title = "Graph Scattering beyond Wavelet Shackles", | ||
+ | booktitle = " | ||
+ | primaryClass = " | ||
+ | eprint = {2301.11456}, | ||
+ | eprinttype = {arXiv}, | ||
+ | year = " | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | @Article{1dPhotDirac, | ||
+ | author = {Koke, Christian and Noh, Changsuk and Angelakis, Dimitris}, | ||
+ | title = {Dirac equation in 2-dimensional curved spacetime, particle creation, and coupled waveguide arrays}, | ||
+ | journal = {Annals of Physics}, | ||
+ | year = {2016}, | ||
+ | volume = {374}, | ||
+ | eprint = {1607.04821}, | ||
+ | eprinttype = {arXiv}, | ||
+ | pages = {162--178}, | ||
+ | } | ||
+ | |||
+ | @Article{2dPhotDirac, | ||
+ | author = {Koke, Christian and Noh, Changsuk and Angelakis, Dimitris}, | ||
+ | title = {Dirac equation on a square waveguide lattice with site-dependent coupling strengths and the gravitational Aharonov-Bohm effect}, | ||
+ | journal = {Physical Review A}, | ||
+ | year = {2020}, | ||
+ | volume = {102}, | ||
+ | eprint = {1909.12543}, | ||
+ | eprinttype = {arXiv}, | ||
+ | pages = {013514}, | ||
+ | } | ||
+ | |||
+ | @article{Wimmer2023, | ||
+ | title={Scale-Equivariant Deep Learning for 3D Data}, | ||
+ | author={Wimmer, | ||
+ | year={2023}, | ||
+ | journal = {arXiv preprint}, | ||
+ | eprint={2304.05864}, | ||
+ | eprinttype={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | keywords={deep learning, equivariant deep learning, scale-equivariance, | ||
+ | } | ||
+ | |||
+ | @inproceedings{pfeiffer2019visual, | ||
+ | title={Visual person understanding through multi-task and multi-dataset learning}, | ||
+ | author={Pfeiffer, | ||
+ | booktitle={gcpr}, | ||
+ | pages={551--566}, | ||
+ | year={2019}, | ||
+ | organization={Springer International Publishing} | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2020single, | ||
+ | title={Single-Shot Panoptic Segmentation}, | ||
+ | author={Weber, | ||
+ | booktitle={iros}, | ||
+ | pages={8476--8483}, | ||
+ | year={2020} | ||
+ | } | ||
+ | |||
+ | @inproceedings{ovsep20204d, | ||
+ | title={4D generic video object proposals}, | ||
+ | author={O{\v{s}}ep, | ||
+ | booktitle={icra}, | ||
+ | pages={10031--10037}, | ||
+ | year={2020}, | ||
+ | organization={IEEE} | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2021step, | ||
+ | title={STEP: | ||
+ | author={Weber, | ||
+ | booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Track on Datasets and Benchmarks)}, | ||
+ | volume={1}, | ||
+ | year={2021} | ||
+ | } | ||
+ | |||
+ | @inproceedings{aygun20214d, | ||
+ | title={4D Panoptic LiDAR Segmentation}, | ||
+ | author={Aygun, | ||
+ | booktitle={cvpr}, | ||
+ | pages={5527--5537}, | ||
+ | year={2021} | ||
+ | } | ||
+ | |||
+ | @article{weber2021deeplab2, | ||
+ | title={DeepLab2: | ||
+ | author={Weber, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2021} | ||
+ | } | ||
+ | |||
+ | @article{dang2023, | ||
+ | title={Joint {MR} sequence optimization beats pure neural network approaches for spin-echo {MRI} super-resolution}, | ||
+ | author={Dang, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2023}, | ||
+ | keywords={medical imaging, magnetic resonance imaging, pulse sequences, super-resolution, | ||
+ | eprint = {2305.07524}, | ||
+ | eprinttype = {arXiv}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{zaiss2023, | ||
+ | title={{GPT4MR}: | ||
+ | author={Zaiss, | ||
+ | year={2023}, | ||
+ | keywords={medical imaging, magnetic resonance imaging, pulse sequences, deep learning, large language models, prompt engineering}, | ||
+ | url={https:// | ||
+ | booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, | ||
+ | award = {Oral Presentation} | ||
+ | } | ||
+ | |||
+ | @inproceedings {ehm2023non, | ||
+ | booktitle = {Eurographics 2023 - Posters}, | ||
+ | title = {{Non-Separable Multi-Dimensional Network Flows for Visual Computing}}, | ||
+ | author = {Ehm, Viktoria and Cremers, Daniel and Bernard, Florian}, | ||
+ | year = {2023}, | ||
+ | publisher = {The Eurographics Association}, | ||
+ | ISSN = {1017-4656}, | ||
+ | ISBN = {978-3-03868-211-0}, | ||
+ | titleurl = {ehm_vector_high_res.png}, | ||
+ | DOI = {10.2312/ | ||
+ | } | ||
+ | |||
+ | @article{sang2023weight, | ||
+ | title={Weight-Aware Implicit Geometry Reconstruction with Curvature-Guided Sampling}, | ||
+ | author={Sang, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2023}, | ||
+ | titleurl = {sang2023.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{muhle2023learning, | ||
+ | title={Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares}, | ||
+ | author={Muhle, | ||
+ | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
+ | eprint = {2305.09527}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | pages={13102--13112}, | ||
+ | year={2023}, | ||
+ | keywords = {pnec, vo, vslam, deep learning}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | @inproceedings{lidarsynthesis2023, | ||
+ | title={LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels}, | ||
+ | booktitle = {IEEE 26th International Conference on Intelligent Transportation Systems}, | ||
+ | author={Schmidt, | ||
+ | year = {2023}, | ||
+ | |||
+ | | ||
+ | note = {{<a href=" | ||
+ | |||
+ | keywords = {neural networks, deep learning, lidar }, | ||
+ | } | ||
+ | |||
+ | @inproceedings{multiagent2023, | ||
+ | title={Multi Agent Navigation in Unconstrained Environments Using a Centralized Attention Based Graphical Neural Network Controller}, | ||
+ | booktitle = {IEEE 26th International Conference on Intelligent Transportation Systems}, | ||
+ | author={Ma, Yining and Khan, Qadeer and Cremers, Daniel}, | ||
+ | year = {2023}, | ||
+ | |||
+ | | ||
+ | note = {{<a href=" | ||
+ | |||
+ | keywords = {neural networks, deep learning, multi-agent control}, | ||
+ | } | ||
+ | |||
+ | @article{vehiclepursuit2023, | ||
+ | author = " | ||
+ | title = " | ||
+ | journal = {{IEEE} Robotics and Automation Letters ({RA-L})}, | ||
+ | year = " | ||
+ | volume={8}, | ||
+ | number={10}, | ||
+ | pages={6595 - 6602}, | ||
+ | note = {{<a href=" | ||
+ | keywords = {vehicle pursuit, deep learning} | ||
+ | } | ||
+ | |||
+ | @inproceedings{xia2023casspr, | ||
+ | author = {Yan Xia and Mariia Gladkova and Rui Wang and Qinyun Li and Uwe Stilla and Joao F. Henriques and Daniel Cremers }, | ||
+ | title = {CASSPR: Cross Attention Single Scan Place Recognition}, | ||
+ | eprint = {2211.12542}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle=iccv, | ||
+ | year = {2023}, | ||
+ | keywords = {lidar, place recognition, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{xia2024text2loc, | ||
+ | title={Text2Loc: | ||
+ | author={Xia, | ||
+ | booktitle=cvpr, | ||
+ | keywords={3d localization, | ||
+ | note={{< | ||
+ | year={2024} | ||
+ | } | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{koke2024holonets, | ||
+ | title={HoloNets: | ||
+ | author={Christian Koke and Daniel Cremers}, | ||
+ | | ||
+ | year={2024}, | ||
+ | eprint={2310.02232}, | ||
+ | eprinttype={arXiv}, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | primaryClass={cs.LG} | ||
+ | } | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | @inproceedings{koke2023holonets, | ||
+ | title={HoloNets: | ||
+ | author={Christian Koke and Daniel Cremers}, | ||
+ | | ||
+ | award = {Oral Presentation}, | ||
+ | year={2023}, | ||
+ | eprint={2310.02232}, | ||
+ | eprinttype={arXiv}, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | primaryClass={cs.LG} | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{koke2023resolvnet, | ||
+ | title={ResolvNet: | ||
+ | author={Christian Koke and Abhishek Saroha and Yuesong Shen and Marvin Eisenberger and Daniel Cremers}, | ||
+ | | ||
+ | award = {Oral Presentation}, | ||
+ | year={2023}, | ||
+ | eprint={2310.00431}, | ||
+ | eprinttype={arXiv}, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | primaryClass={cs.LG} | ||
+ | } | ||
+ | |||
+ | @inproceedings{schnaus2023learning, | ||
+ | title={Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks}, | ||
+ | author={Schnaus, | ||
+ | booktitle={International Conference on Machine Learning}, | ||
+ | pages={30252--30284}, | ||
+ | year={2023}, | ||
+ | organization={PMLR} | ||
+ | } | ||
+ | |||
+ | @inproceedings{gao2023sigma, | ||
+ | author = { Maolin Gao and Paul Roetzer and Marvin Eisenberger and Zorah L\" | ||
+ | title = { {SIGMA}: Quantum Scale-Invariant Global Sparse Shape Matching}, | ||
+ | booktitle = {International Conference on Computer Vision (ICCV)}, | ||
+ | year = 2023, | ||
+ | keywords = {Shape Analysis, Geometry Processing, Global Optimisation, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | | ||
+ | @inproceedings{Roetzer_et_al_cvpr22, | ||
+ | title={A scalable combinatorial solver for elastic geometrically consistent 3d shape matching}, | ||
+ | author={Roetzer, | ||
+ | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
+ | pages={428--438}, | ||
+ | year={2022}} | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{gao2023sigma, | ||
+ | author = { Maolin Gao and Paul Roetzer and Marvin Eisenberger and Zorah L\" | ||
+ | title = { {SIGMA}: Quantum Scale-Invariant Global Sparse Shape Matching}, | ||
+ | booktitle = {International Conference on Computer Vision (ICCV)}, | ||
+ | year = 2023, | ||
+ | keywords = {Shape Analysis, Geometry Processing, Global Optimisation, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @misc{deka2023erasing, | ||
+ | title={Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis}, | ||
+ | author={Mreenav Shyam Deka and Lu Sang and Daniel Cremers}, | ||
+ | year={2023}, | ||
+ | eprint={2311.17634}, | ||
+ | archivePrefix={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | titleurl = {deka2023.png}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | } | ||
+ | } | ||
+ | |||
+ | @misc{komorowicz2023coloring, | ||
+ | title={Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography}, | ||
+ | author={David Komorowicz and Lu Sang and Ferdinand Maiwald and Daniel Cremers}, | ||
+ | year={2023}, | ||
+ | eprint={2311.17810}, | ||
+ | archivePrefix={arXiv}, | ||
+ | primaryClass={cs.CV}, | ||
+ | titleurl = {komorowicz2023.png}, | ||
+ | note = { | ||
+ | {<a href=" | ||
+ | } | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{compreason2024, | ||
+ | title={Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining}, | ||
+ | booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV}, | ||
+ | author={Sahin, | ||
+ | year = {2024}, | ||
+ | |||
+ | | ||
+ | note = {{<a href=" | ||
+ | |||
+ | keywords = {neural networks, deep learning, Large Language Models}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{hayler2023s4c, | ||
+ | title={S4C: Self-Supervised Semantic Scene Completion with Neural Fields}, | ||
+ | author={Hayler, | ||
+ | booktitle={2024 International Conference on 3D Vision (3DV)}, | ||
+ | year={2024}, | ||
+ | eprint={2310.07522}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @InProceedings{wimbauer2023cache, | ||
+ | title={Cache Me if You Can: Accelerating Diffusion Models through Block Caching}, | ||
+ | author={Wimbauer, | ||
+ | booktitle=cvpr, | ||
+ | year={2024}, | ||
+ | eprint={2312.03209}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | note={{< | ||
+ | } | ||
+ | |||
+ | @article{ehm2023geometrically, | ||
+ | title={Geometrically Consistent Partial Shape Matching}, | ||
+ | author={Ehm, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | titleurl = {2023_ehm_geo_cons.png}, | ||
+ | year={2023} | ||
+ | } | ||
+ | |||
+ | @article{multivehicle2023, | ||
+ | title={Multi-vehicle trajectory prediction and control at intersections using state and intention information}, | ||
+ | author={Zhu, | ||
+ | journal={Neurocomputing}, | ||
+ | pages={127220}, | ||
+ | year={2024}, | ||
+ | publisher={Elsevier}, | ||
+ | | ||
+ | note = {{<a href=" | ||
+ | | ||
+ | keywords = {deep learning}, | ||
+ | | ||
+ | |||
+ | |||
+ | } | ||
+ | |||
+ | @inproceedings{solonets2024analytical, | ||
+ | title={An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment}, | ||
+ | author={Solonets, | ||
+ | booktitle={{International Conference on Learning Representations (ICLR)}}, | ||
+ | month={May}, | ||
+ | address={Vienna, | ||
+ | year = {2024}, | ||
+ | award = {Oral Presentation}, | ||
+ | note = {To appear}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2024jpeg, | ||
+ | title={Differentiable {JPEG}: The Devil is in the Details}, | ||
+ | author={Reich, | ||
+ | booktitle={wacv}, | ||
+ | pages={4126--4135}, | ||
+ | year={2024}, | ||
+ | note={{< | ||
+ | titleurl={reich2024jpeg.png} , | ||
+ | eprint = {2309.06978}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV} | ||
+ | } | ||
+ | |||
+ | @article{reich2022cell, | ||
+ | title={Yeast cell segmentation in microstructured environments with deep learning}, | ||
+ | author={Prangemeier, | ||
+ | journal={Biosystems}, | ||
+ | volume={211}, | ||
+ | pages={104557}, | ||
+ | year={2022}, | ||
+ | publisher={Elsevier}, | ||
+ | titleurl={reich2022cell.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2020cellb, | ||
+ | title={Attention-Based Transformers for Instance Segmentation of Cells in Microstructures}, | ||
+ | author={Prangemeier, | ||
+ | booktitle={2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, | ||
+ | pages={700--707}, | ||
+ | year={2020}, | ||
+ | eprint={2011.09763}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | titleurl={reich2020cellb.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2020cella, | ||
+ | title={Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning}, | ||
+ | author={Prangemeier, | ||
+ | booktitle={2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)}, | ||
+ | pages={1--8}, | ||
+ | year={2020}, | ||
+ | eprint={2011.08062}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={q-bio.QM}, | ||
+ | titleurl={reich2020cella.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2022compat, | ||
+ | title={Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels}, | ||
+ | author={Gul, | ||
+ | booktitle={Medical Imaging 2022: Digital and Computational Pathology}, | ||
+ | volume={12039}, | ||
+ | organization={International Society for Optics and Photonics}, | ||
+ | publisher={SPIE}, | ||
+ | pages={366--373}, | ||
+ | year={2022}, | ||
+ | eprint={2210.09021}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | titleurl={reich2022compat.png} | ||
+ | } | ||
+ | |||
+ | @article{reich2022ecg, | ||
+ | title={Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in Medical Machine Learning}, | ||
+ | author={Rohr, | ||
+ | journal={Physiological Measurement}, | ||
+ | volume = {43}, | ||
+ | number = {7}, | ||
+ | pages = {074001}, | ||
+ | year={2022}, | ||
+ | publisher={IOP Publishing}, | ||
+ | titleurl={reich2022ecg.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2023cella, | ||
+ | title={An Instance Segmentation Dataset of Yeast Cells in Microstructures}, | ||
+ | author={Reich, | ||
+ | booktitle={45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, | ||
+ | note={{< | ||
+ | pages={1-4}, | ||
+ | year={2023}, | ||
+ | titleurl={reich2023cella.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2023cellb, | ||
+ | title={The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures}, | ||
+ | author={Reich, | ||
+ | booktitle={IEEE/ | ||
+ | note={{< | ||
+ | pages={3940--3951}, | ||
+ | year={2023}, | ||
+ | eprint={2304.07597}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | titleurl={reich2023cellb.png} | ||
+ | } | ||
+ | |||
+ | |||
+ | @inproceedings{reich2023ecg, | ||
+ | title={On the Atrial Fibrillation Detection Performance of ECG-DualNet}, | ||
+ | author={Reich, | ||
+ | booktitle={45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-Page Paper, medRxiv}, | ||
+ | year={2023}, | ||
+ | titleurl={reich2023ecg.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2021gan, | ||
+ | title={Multi-StyleGAN: | ||
+ | author={Reich, | ||
+ | booktitle={{International Conference on Medical image computing and computer-assisted intervention (MICCAI)}}, | ||
+ | note={{< | ||
+ | year={2021}, | ||
+ | pages={476--486}, | ||
+ | eprint={2106.08285}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={cs.CV}, | ||
+ | titleurl={reich2021gan.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich20213dseg, | ||
+ | title={OSS-Net: | ||
+ | author={Reich, | ||
+ | booktitle={British Machine Vision Conference (BMVC)}, | ||
+ | note={{< | ||
+ | year={2021}, | ||
+ | eprint={2110.10640}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={eess.IV}, | ||
+ | titleurl={reich20213dseg.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2023eeg, | ||
+ | title={Transformer Network with Time Prior for Predicting Clinical Outcome from EEG of Cardiac Arrest Patients}, | ||
+ | author={Rohr, | ||
+ | booktitle={50th Computing in Cardiology Conference (CinC)}, | ||
+ | year={2023}, | ||
+ | titleurl={reich2023eeg.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2023dvcc, | ||
+ | title={Deep Video Codec Control}, | ||
+ | author={Reich, | ||
+ | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), in press}, | ||
+ | year={2024}, | ||
+ | eprint={2308.16215}, | ||
+ | eprinttype={arXiv}, | ||
+ | eprintclass={eess.IV}, | ||
+ | titleurl={reich2023dvcc.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{reich2024coding, | ||
+ | title={A Perspective on Deep Vision Performance with Standard Image and Video Codecs}, | ||
+ | author={Reich, | ||
+ | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), in press}, | ||
+ | year={2024}, | ||
+ | titleurl={reich2024coding.png} | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2024finslerlaplacebeltrami, | ||
+ | title = {Finsler-Laplace-Beltrami Operators with Application to Shape Analysis}, | ||
+ | author = {Weber, Simon and Dages, Thomas and Gao, Maolin and Cremers, Daniel}, | ||
+ | booktitle= cvpr, | ||
+ | keywords = {shape analysis, finsler manifold}, | ||
+ | titleurl = {weber_finsler.png}, | ||
+ | year = {2024}, | ||
+ | eprint = {2404.03999}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{weber2024flattening, | ||
+ | title = {Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball}, | ||
+ | author = {Weber, Simon and Zöngür, Barış and Araslanov, Nikita and Cremers, Daniel}, | ||
+ | booktitle= cvpr, | ||
+ | keywords = {segmentation, | ||
+ | titleurl = {weber_hyperbolic.png}, | ||
+ | year = {2024}, | ||
+ | | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | } | ||
+ | |||
+ | @article{saroha2024gaussian, | ||
+ | title={Gaussian Splatting in Style}, | ||
+ | author={Saroha, | ||
+ | journal={arXiv preprint arXiv: | ||
+ | year={2024}, | ||
+ | titleurl = {gaussian-style-schema.png}, | ||
+ | eprint = {2403.08498}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{han2024kdbts, | ||
+ | title={Boosting Self-Supervision for Single-View Scene Completion | ||
+ | via Knowledge Distillation}, | ||
+ | author={Han, | ||
+ | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
+ | year={2024}, | ||
+ | eprint = {2404.07933}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{chen2024leap, | ||
+ | title={LEAP-VO: | ||
+ | author={Chen, | ||
+ | journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
+ | year={2024} | ||
+ | } | ||
+ | |||
+ | @inproceedings{gurumurthy24v2v, | ||
+ | author | ||
+ | title = {From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers}, | ||
+ | booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
+ | year = {2024}, | ||
+ | titleurl = {gurumurthy24v2v} | ||
+ | } | ||
+ | |||
+ | @inproceedings{schult24controlroom3d, | ||
+ | author | ||
+ | title = {ControlRoom3D: | ||
+ | booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
+ | year = {2024}, | ||
+ | titleurl = {schult2024controlroom3d} | ||
+ | } | ||
+ | |||
+ | @inproceedings{brahimi2024sparse, | ||
+ | author = {Brahimi, Mohammed and Haefner, Bjoern and Ye, Zhenzhang and Goldluecke, Bastian | ||
+ | and Cremers, Daniel}, | ||
+ | title = {Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo}, | ||
+ | booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
+ | year = {2024}, | ||
+ | titleurl = {brahimi2024sparse} | ||
+ | eprint = {2404.00098}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note = | ||
+ | { | ||
+ | {<a href="/ | ||
+ | }, | ||
+ | keywords={d-reconstruction, | ||
+ | } |