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Geometry Processing

Geometry processing is concerned with the acquisition, analysis and manipulation of geometric data. The field is very broad and algorithms aim at improving 3D reconstructions, finding correspondences between objects, shape interpolation, or analysing physical properties of scanned data. Many applications in geometry processing have counterparts in image processing, e.g. object detection, but geometric data poses special challenges in terms of how to formulate mathematical concepts on discretized domains, and choosing the best data structures and shape representations for each application.

Our group is especially interested in 3D geometric data and works mainly in the directions of 3D shape analysis, point cloud processing and inference from 3D data. But our members have worked on a wide range of subtopics, so feel free to reach out to us if you are interested in any geometry processing related topic!

3D Shape Analysis Point Cloud Processing Inference from 3D data
Non-Rigid Correspondence
Non-Rigid Correspondence
Primitive Detection
Primitive Detection
Material Reconstruction
Material Reconstruction

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2021 2020 2019 2018 2017 2016 2015 2013 
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
[]i3DMM: Deep Implicit 3D Morphable Model of Human Heads (T Yenamandra, A Tewari, F Bernard, HP Seidel, M Elgharib, D Cremers and C Theobalt), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([Project page],[ArXiv]) [bibtex]Oral Presentation
2021 2020 2019 2018 2017 2016 2015 2013 
2020
Journal Articles
[]From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds (C. Sommer, Y. Sun, L. J. Guibas, D. Cremers and T. Birdal), In IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA), volume 5, 2020.  [bibtex] [doi] [arXiv:2001.07360]
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
[]PrimiTect: Fast Continuous Hough Voting for Primitive Detection (C. Sommer, Y. Sun, E. Bylow and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2020.  [bibtex] [doi] [arXiv:2005.07457]
[]Correspondence-Free Material Reconstruction using Sparse Surface Constraints (S. Weiss, R. Maier, D. Cremers, R. Westermann and N. Thuerey), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [pdf]
[]Hamiltonian Dynamics for Real-World Shape Interpolation (M. Eisenberger and D. Cremers), In European Conference on Computer Vision (ECCV), 2020. ([arXiv] [code]) [bibtex]Spotlight Presentation
[]Smooth Shells: Multi-Scale Shape Registration with Functional Maps (M. Eisenberger, Z. Lähner and D. Cremers), In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020. ([pdf] [arXiv] [code]) [bibtex]Oral Presentation
2021 2020 2019 2018 2017 2016 2015 2013 
2019
Journal Articles
[]Functional Maps Representation on Product Manifolds (E Rodolà, Z Lähner, AM. Bronstein, MM. Bronstein and J Solomon), In Computer Graphics Forum, volume 38, 2019. ((Presented at Symposium on Geometry Processing (SGP)) [arxiv]) [bibtex] [pdf]
Conference and Workshop Papers
[]Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction (S. Weiss, R. Maier, R. Westermann, D. Cremers and N. Thuerey), In arXiv preprint arXiv:1910.01812, 2019. ([pdf]) [bibtex] [arXiv:1910.01812]
[]Divergence-Free Shape Correspondence by Deformation (M. Eisenberger, Z. Lähner and D. Cremers), In Computer Graphics Forum, volume 38, 2019. ([arxiv]) [bibtex] [pdf]
[]Shape Correspondence with Isometric and Non-Isometric Deformations (R. Dyke, C. Stride, Y.-K. Lai, P. L. Rosin, M. Aubry, A. Boyarski, A. M. Bronstein, M. M. Bronstein, D. Cremers, M. Fisher, T. Groueix, D. Guo, V. G. Kim, R. Kimmel, Z. Lähner, K. Li, O. Litany, T. Remez, E. Rodolà, B. C. Russell, Y. Sahillioglu, R. Slossberg, G. K. L. Tam, M. Vestner, Z. Wu and J. Yang), In 12th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2019, Genoa, Italy, May 5-6, 2019 (S Biasotti, G Lavoué, RC. Veltkamp, eds.), Eurographics Association, 2019.  [bibtex]
2021 2020 2019 2018 2017 2016 2015 2013 
2018
Conference and Workshop Papers
[]Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis (V. Estellers, F. Schmidt and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2018. ([Code]) [bibtex] [pdf]Received the Best Paper Award at 3DV 2018
[]DeepWrinkles: Accurate and Realistic Clothing Modeling (Z. Lähner, D. Cremers and T. Tung), In European Conference on Computer Vision (ECCV), 2018. ([Homepage], [Oral Presentation]) [bibtex] [pdf]Oral Presentation
[]Joint Representation of Primitive and Non-primitive Objects for 3D Vision (C. Sommer and D. Cremers), In 2018 International Conference on 3D Vision, 3DV 2018, Verona, Italy, September 5-8, 2018, IEEE Computer Society, 2018.  [bibtex] [doi]
2021 2020 2019 2018 2017 2016 2015 2013 
2017
Journal Articles
[]Regularized Pointwise Map Recovery from Functional Correspondence (E Rodolà, M Möller and D Cremers), In Comput. Graph. Forum, volume 36, 2017.  [bibtex]
Conference and Workshop Papers
[]A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching (F. Bernard, F. R. Schmidt, J. Thunberg and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]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
[]Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space (M. Vestner, R. Litman, E. Rodola, A. Bronstein and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([Code], also check the related github repository) [bibtex] [pdf]
2021 2020 2019 2018 2017 2016 2015 2013 
2016
Book Chapters
[]Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence (M. Vestner, E. Rodolà, T. Windheuser, RBS. Bulò and D. Cremers), Chapter in Perspectives in Shape Analysis, Springer, 2016.  [bibtex]
Conference and Workshop Papers
[]A Convex Solution to Spatially-Regularized Correspondence Problems (T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016.  [bibtex] [pdf]
[]Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding (I. Chiotellis, R. Triebel, T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([code]) [bibtex] [pdf]
[] 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]
2021 2020 2019 2018 2017 2016 2015 2013 
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]
2021 2020 2019 2018 2017 2016 2015 2013 
2013
Journal Articles
[]Stable and Fast Techniques for Unambiguous Compound Phase Coding (A. Torsello, A. Albarelli and E. Rodola), In Image and Vision Computing, volume 31, 2013.  [bibtex] [doi] [pdf]
[]A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes (E. Rodola, A. Albarelli, F. Bergamasco and A. Torsello), In International Journal of Computer Vision, Springer US, volume 102, 2013.  [bibtex] [doi] [pdf]
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04.06.2021

Bernt Schiele (Max Planck Institute for Informatics) will give a talk in the TUM AI lecture series on June 10th, 3pm! Livestream

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French-German Machine Learning Symposium

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The French-German Machine Learning Symposium aims to strengthen interactions and inspire collaborations between both countries. We invited some of the leading ML researchers from France and Germany to this two-day symposium to give a glimpse into their research, and engage in discussions on the future of machine learning and how to strengthen research collaborations in ML between France and Germany.

The list of speakers includes Yann LeCun, Cordelia Schmid, Jean-Bernard Lasserre, Bernhard Schölkopf, and many more! For the full program please visit the webpage.

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Hao Li (Pinscreen) will give a talk in the TUM AI lecture series on April 22nd, 8pm! Livestream

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