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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini · Jonathan Masci · Emanuele Rodolà · Michael Bronstein

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #165 #None

Convolutional neural networks have achieved extraordinary results in many computer vision and pattern recognition applications; however, their adoption in the computer graphics and geometry processing communities is limited due to the non-Euclidean structure of their data. In this paper, we propose Anisotropic Convolutional Neural Network (ACNN), a generalization of classical CNNs to non-Euclidean domains, where classical convolutions are replaced by projections over a set of oriented anisotropic diffusion kernels. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes, a fundamental problem in geometry processing, arising in a wide variety of applications. We tested ACNNs performance in very challenging settings, achieving state-of-the-art results on some of the most difficult recent correspondence benchmarks.

Author Information

Davide Boscaini (University of Lugano)

Davide Boscaini (MSc 2013, University of Verona, Italy) is a PhD candidate in the Institute of Computational Science, Faculty of Informatics at the University of Lugano (USI). His main research interests lie in the field of 3D shape analysis and synthesis, with a preference for approaches inspired by geometric intuitions. Recently, his research focussed on merging spectral approaches and machine learning techniques to develop novel algorithms for 3D shape analysis. A notable example includes the first intrinsic extension of the popular Convolutional Neural Networks paradigm to non-Euclidean domains.

Jonathan Masci (NNAISENSE)
Emanuele Rodolà (University of Lugano)
Michael Bronstein (University of Lugano)

Michael Bronstein is an associate professor of Informatics at USI Lugano in Switzerland, associate professor of Applied Mathematics at Tel Aviv University in Israel, and a Principal Engineer at the Intel Perceptual Computing. Michael got his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford, Harvard, and MIT. He is a Senior Member of the IEEE, alumnus of the Technion Excellence Program and the Academy of Achievement, ACM Distinguished Speaker, and a member of the Young Academy of Europe. His research appeared in the international media such as CNN and was recognized by numerous prestigious awards, including several best paper awards, three ERC grants (Starting Grant 2012, Proof of Concept Grant 2016, and Consolidator Grant 2016), Google Faculty Research Award (2016), Radcliffe Fellowship from the Institute for Advanced Study at Harvard University (2017), and Rudolf Diesel Industrial Fellowship from TU Munich (2017). In 2014, he was invited as a Young Scientist to the World Economic Forum, an honor bestowed on forty world's leading scientists under the age of forty. Michael is the author of over 100 papers in top scientific journals and conferences, and inventor of over 25 granted patents. He has chaired over a dozen of conferences and workshops in his field, and has served as area chair at ECCV 2016 and ICCV 2017 and as associate editor of the Computer Vision and Image Understanding journal. Besides academic work, Michael is actively involved in the industry. He has co-founded and served in leading technical and management positions at several startup companies, including Invision, an Israeli startup developing 3D sensing technology acquired by Intel in 2012.

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