Over the last years, the availability of devices for the acquisition of three-dimensional data like laser-scanners, RGB-D Vision or medical imaging devices has dramatically increased. This brings about the need for efficient algorithms to analyze three-dimensional shapes.

Our research is focused on reliable and efficient methods for the automatic interpretation of non-rigid three-dimensional shapes. In particular, we have been working on novel approaches to Shape Matching and on the design of Feature Descriptors.

The goal of shape matching is to register corresponding surface regions of two given three-dimensional shapes. This means for example to identify the hands, the feet and the head of two human figures. Once two shapes are registered, one can infer morphs between them. The video shows examples of such morphs, e.g. interpolating between a samba dancer and a hip hop dancer. In each of these example sequences, a registration of the first and the last shape has been computed, all intermediate frames are obtained by linear interpolation. The colors visualize regions which have been identified with each other.

A registration of two shapes can be the building block for measuring similarity of shapes and for performing shape statistics.

Mathematically, the task is to find a mapping from the boundary surface of one shape to the other one. This typically results in a very difficult, highly non-convex optimization problem.

We are currently working on efficient algorithms for shape matching with a focus on geometric consistency guarantees and on global optimization.

The goal of feature descriptors is to characterize each point on an object's surface regarding its relation to the entire shape. The feature vectors of four different points of a cat are shown above.

In our research we aim at feature descriptors which are robust to shape articulations while capturing as much information as possible. A very powerful mathematical tool for this task is the eigendecomposition of the Laplace–Beltrami operator.

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2024
Conference and Workshop Papers
[]		Finsler-Laplace-Beltrami Operators with Application to Shape Analysis (S Weber, T Dagès, M Gao and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page][video]) [bibtex] [arXiv:2404.03999]
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2023
Conference and Workshop Papers
[]		SIGMA: Quantum Scale-Invariant Global Sparse Shape Matching (M Gao, P Roetzer, M Eisenberger, Z Lähner, M Moeller, D Cremers and F Bernard), In International Conference on Computer Vision (ICCV), 2023. ([pdf]) [bibtex]
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2022
Conference and Workshop Papers
[]		Intrinsic Neural Fields: Learning Functions on Manifolds (L Koestler, D Grittner, M Moeller, D Cremers and Z Lähner), In European Conference on Computer Vision (ECCV), 2022. (Code will be released soon.) [bibtex] [arXiv:2203.07967] [pdf]
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2021
Conference and Workshop Papers
[]		Isometric Multi-Shape Matching (M Gao, Z Lähner, J Thunberg, D Cremers and F Bernard), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arxiv]) [bibtex]Oral Presentation
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2020
Conference and Workshop Papers
[]		Unsupervised Dense Shape Correspondence using Heat Kernels (M Aygün, Z Lähner and D Cremers), In International Conference on 3D Vision (3DV), 2020. ([arxiv]) [bibtex] [pdf]
[]		Simulated Annealing for 3D Shape Correspondence (B Holzschuh, Z Lähner and D Cremers), In International Conference on 3D Vision (3DV), 2020.  [bibtex] [pdf]Oral Presentation
[]		Hamiltonian Dynamics for Real-World Shape Interpolation (M. Eisenberger and D. Cremers), In European Conference on Computer Vision (ECCV), 2020. ([arXiv] [code]) [bibtex]Spotlight Presentation
[]		DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020. ([video][presentation][project page][supplementary][arxiv]) [bibtex] [pdf]
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2017
Conference and Workshop Papers
[]		Efficient Deformable Shape Correspondence via Kernel Matching (M. Vestner, Z. Lähner, A. Boyarski, O. Litany, R. Slossberg, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, R. Kimmel and D. Cremers), In International Conference on 3D Vision (3DV), 2017. ([arxiv],[Code]) [bibtex] [pdf]Oral Presentation
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2016
Conference and Workshop Papers
[]		SHREC’16: Partial Matching of Deformable Shapes (L. Cosmo, E. Rodola, M. M. Bronstein, A. Torsello, D. Cremers and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. (to appear) [bibtex] [pdf]
[]		SHREC’16: Matching of Deformable Shapes with Topological Noise (Z. Lähner, E. Rodola, M. M. Bronstein, D. Cremers, O. Burghard, L. Cosmo, A. Dieckmann, R. Klein and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. ([Dataset]) [bibtex] [pdf] [pdf]
[]		Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (Z. Lähner, E. Rodola, F. R. Schmidt, M. M. Bronstein and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([Code], [Homepage]) [bibtex] [pdf] [pdf]
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2015
Journal Articles
[]		Fast and Accurate Surface Alignment through an Isometry-Enforcing Game (A. Albarelli, E. Rodola and A. Torsello), In Pattern Recognition, Elsevier, volume 48, 2015.  [bibtex] [doi] [pdf]
Conference and Workshop Papers
[]		A Fast Projection Method for Connectivity Constraints in Image Segmentation (J. Stühmer and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker, eds.), 2015.  [bibtex] [pdf]
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2013
Journal Articles
[]		A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes (E. Rodola, A. Albarelli, F. Bergamasco and A. Torsello), In International Journal of Computer Vision, Springer US, volume 102, 2013.  [bibtex] [doi] [pdf]
Conference and Workshop Papers
[]		Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs (J. Stühmer, P. Schröder and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]Oral Presentation
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2012
Conference and Workshop Papers
[]		A game-theoretic approach to deformable shape matching (E. Rodola, A.M. Bronstein, A. Albarelli, F. Bergamasco and A. Torsello), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [doi] [pdf]
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2011
Book Chapters
[]		Image Segmentation with Shape Priors: Explicit Versus Implicit Representations (D. Cremers), Chapter in Handbook of Mathematical Methods in Imaging, Springer, 2011.  [bibtex] [pdf]
Conference and Workshop Papers
[]		The Wave Kernel Signature: A Quantum Mechanical Approach To Shape Analysis (M. Aubry, U. Schlickewei and D. Cremers), In IEEE International Conference on Computer Vision (ICCV) - Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2011.  [bibtex] [pdf] [code]
[]		Sampling Relevant Points for Surface Registration (A. Torsello, E. Rodola and A. Albarelli), In International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011.  [bibtex] [doi] [pdf]
[]		A Non-Cooperative Game for 3D Object Recognition in Cluttered Scenes (A. Albarelli, E. Rodola and A. Torsello), In International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011.  [bibtex] [doi] [pdf]
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2010
Conference and Workshop Papers
[]		A Game-Theoretic Approach to Fine Surface Registration without Initial Motion Estimation (A. Albarelli, E. Rodola and A. Torsello), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.  [bibtex] [doi] [pdf]
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2009
Journal Articles
[]		Combined region- and motion-based 3D tracking of rigid and articulated objects (T. Brox, B. Rosenhahn, J. Gall and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, 2009.  [bibtex] [pdf]
Conference and Workshop Papers
[]		Efficient Planar Graph Cuts with Applications in Computer Vision (F. R. Schmidt, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. (Note: This version of the paper is modified with respect to the published one: In the published version we had only cited the PhD thesis [2], as the conference paper [3] does not mention Step 13 of Algorithm 2 that restores the important property of T being a rooted tree.) [bibtex] [pdf]Received a CVPR Doctoral Spotlight Award
[]		A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors (F. R. Schmidt and D. Cremers), In Pattern Recognition (Proc. DAGM), 2009.  [bibtex] [pdf]
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2008
Journal Articles
[]		Nonlinear Dynamical Shape Priors for Level Set Segmentation (D. Cremers), In Journal of Scientific Computing, volume 35, 2008.  [bibtex] [pdf]
Conference and Workshop Papers
[]		On Errors-In-Variables Regression with Arbitrary Covariance and its Application to Optical Flow Estimation (B. Andres, C. Nieuwenhuis, D. Kondermann, U. Köthe and R. Hamprecht), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]		Shape Priors in Variational Image Segmentation: Convexity, Lipschitz Continuity and Globally Optimal Solutions (D. Cremers, F. R. Schmidt and F. Barthel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
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2007
Book Chapters
[]		Efficient kernel density estimation of shape and intensity priors for level set segmentation (D. Cremers and M. Rousson), Chapter in Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery (J. S. Suri, A. Farag, eds.), Springer, 2007.  [bibtex] [pdf]
Journal Articles
[]		A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape (D. Cremers, M. Rousson and R. Deriche), In International Journal of Computer Vision, volume 72, 2007.  [bibtex] [pdf]
Conference and Workshop Papers
[]		Nonlinear Dynamical Shape Priors for Level Set Segmentation (D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.  [bibtex] [pdf]
[]		Efficient Shape Matching via Graph Cuts (F. R. Schmidt, E. Toeppe, D. Cremers and Y. Boykov), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 4679, 2007.  [bibtex] [pdf]
[]		Intrinsic Mean for Semimetrical Shape Retrieval via Graph Cuts (F. R. Schmidt, E. Toeppe, D. Cremers and Y. Boykov), In Pattern Recognition (Proc. DAGM), Springer, volume 4713, 2007.  [bibtex] [pdf]
[]		Fast Matching of Planar Shapes in Sub-cubic Runtime (F. R. Schmidt, D Farin and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2007.  [bibtex] [pdf]
[]		Globally Optimal Image Segmentation with an Elastic Shape Prior (T. Schoenemann and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2007.  [bibtex] [pdf]
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2006
Journal Articles
[]		Integral invariants for shape matching (S. Manay, D. Cremers, B.-W. Hong, A. Yezzi and S. Soatto), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
[]		Dynamical statistical shape priors for level set based tracking (D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
Conference and Workshop Papers
[]		4D shape priors for level set segmentation of the left myocardium in SPECT sequences (T. Kohlberger, D. Cremers, M. Rousson and R. Ramaraj), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 4190, 2006.  [bibtex] [pdf]
[]		Statistical priors for combinatorial optimization: efficient solutions via Graph Cuts (D. Cremers and L. Grady), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3953, 2006.  [bibtex] [pdf]
[]		Shape Matching by Variational Computation of Geodesics on a Manifold (F. R. Schmidt, M. Clausen and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, volume 4174, 2006.  [bibtex] [pdf]
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2005
Conference and Workshop Papers
[]		Efficient kernel density estimation of shape and intensity priors for level set segmentation (M. Rousson and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 1, 2005.  [bibtex] [pdf]
[]		One-shot integral invariant shape priors for variational segmentation (S. Manay, D. Cremers, A. J. Yezzi and S. Soatto), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (A. Rangarajan, B. Vemuri, A. L. Yuille, eds.), volume 3757, 2005.  [bibtex]
[]		Dynamical statistical shape priors for level set based tracking (D. Cremers and G. Funka-Lea), In Intl. Workshop on Variational and Level Set Methods (N. Paragios, F. Faugeras, T. Chan, C. Schnörr, eds.), Springer, volume 3752, 2005. (210–221) [bibtex] [pdf]
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2004
Conference and Workshop Papers
[]		Multiphase dynamic labeling for variational recognition-driven image segmentation (D. Cremers, N. Sochen and C. Schnörr), In European Conference on Computer Vision (ECCV) (T. Pajdla, V. Hlavac, eds.), Springer, volume 3024, 2004.  [bibtex] [pdf]
[]		Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk (D. Cremers, S. J. Osher and S. Soatto), In Pattern Recognition (Proc. DAGM) (C. E. Rasmussen, ed.), Springer, volume 3175, 2004.  [bibtex] [pdf]
[]		High accuracy optical flow estimation based on a theory for warping (T. Brox, A. Bruhn, N. Papenberg and J. Weickert), In European Conference on Computer Vision (ECCV) (T. Pajdla, J. Matas, eds.), Springer, volume 3024, 2004.  [bibtex] [pdf]Received "The Longuet-Higgins Best Paper Award"
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2003
Journal Articles
[]		Shape Statistics in Kernel Space for Variational Image Segmentation (D. Cremers, T. Kohlberger and C. Schnörr), In Pattern Recognition, volume 36, 2003.  [bibtex] [pdf]Awarded Best Paper of the Year 2003
Conference and Workshop Papers
[]		Towards Recognition-based Variational Segmentation Using Shape Priors and Dynamic Labeling (D. Cremers, N. Sochen and C. Schnörr), In Scale-Space Methods in Computer Vision (L. D. Griffin, M. Lillholm, eds.), Springer, volume 2695, 2003.  [bibtex] [pdf]
[]		A pseudo-distance for shape priors in level set segmentation (D. Cremers and S. Soatto), In IEEE 2nd Int. Workshop on Variational, Geometric and Level Set Methods (N. Paragios, ed.), 2003.  [bibtex] [pdf]
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2002
Journal Articles
[]		Diffusion Snakes: Introducing statistical shape knowledge into the Mumford–Shah functional (D. Cremers, F. Tischhäuser, J. Weickert and C. Schnörr), In International Journal of Computer Vision, volume 50, 2002.  [bibtex] [pdf]
Conference and Workshop Papers
[]		Nonlinear shape statistics in Mumford–Shah based segmentation (D. Cremers, T. Kohlberger and C. Schnörr), In European Conference on Computer Vision (ECCV) (A. Heyden, others, eds.), Springer, volume 2351, 2002.  [bibtex] [pdf]
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2000
Conference and Workshop Papers
[]		Diffusion Snakes using statistical shape knowledge (D. Cremers, C. Schnörr, J. Weickert and C. Schellewald), In Algebraic Frames for the Perception-Action Cycle (G. Sommer, Y.Y. Zeevi, eds.), Springer, volume 1888, 2000.  [bibtex]
[]		Learning of translation invariant shape knowledge for steering diffusion snakes (D. Cremers, C. Schnörr, J. Weickert and C. Schellewald), In Dynamische Perzeption (G. Baratoff, H. Neumann, eds.), Infix, volume 9, 2000.  [bibtex]