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Computer Vision Group
Faculty of Informatics
Technical University of Munich

Technical University of Munich

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Book Chapters
2016
Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence (M. Vestner, E. Rodolà, T. Windheuser, Rota Bulo S. Bulò, D. Cremers), Chapter in Perspectives in Shape Analysis, Springertopic=Shape Analysis, Shape Matching, Machine Learning, 2016. [bib]
Journal Articles
2016
Bayesian Inference of Bijective Non-Rigid Shape Correspondence (M. Vestner, R. Litman, A. Bronstein, E. Rodola, D. Cremers), In arXiv preprint arXiv:1607.03425, 2016. ([slides]) [bib] [pdf]
Conference and Workshop Papers
2017
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, D. Cremers), In International Conference on 3D Vision (3DV), 2017. ([arxiv],[Code]) [bib]
Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space (M. Vestner, R. Litman, E. Rodola, A. Bronstein, D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([Code]) [bib] [pdf]
2014
Optimal Intrinsic Descriptors for Non-Rigid Shape Analysis (T. Windheuser, M. Vestner, E. Rodola, R. Triebel, D. Cremers), In British Machine Vision Conference (BMVC), 2014. [bib] [pdf]
Dense Non-Rigid Shape Correspondence Using Random Forests (E. Rodola, S. Rota Bulo, T. Windheuser, M. Vestner, D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [bib] [pdf] [code]
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