Self-supervised Learning of Dense Shape Correspondence

6 December 2018

Papers citing "Self-supervised Learning of Dense Shape Correspondence"

9 / 9 papers shown

Title
Non-Rigid Puzzles Or Litany Emanuele Rodolà Alexander M. Bronstein Michael M. Bronstein Daniel Cremers 3DV 21 76 0 26 Nov 2020
3D-CODED : 3D Correspondences by Deep Deformation Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell Mathieu Aubry 3DPC 3DV 144 328 0 13 Jun 2018
Efficient Deformable Shape Correspondence via Kernel Matching Zorah Lähner Matthias Vestner A. Boyarski Or Litany Ron Slossberg ... Emanuele Rodolà A. Bronstein M. Bronstein Ron Kimmel Daniel Cremers 3DPC 31 120 0 25 Jul 2017
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence Or Litany Tal Remez Emanuele Rodolà A. Bronstein M. Bronstein 3DPC 100 284 0 27 Apr 2017
Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space Matthias Vestner R. Litman Emanuele Rodolà A. Bronstein Daniel Cremers 60 130 0 03 Jan 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 364 1,817 0 25 Nov 2016
Learning shape correspondence with anisotropic convolutional neural networks Davide Boscaini Jonathan Masci Emanuele Rodolà M. Bronstein 3DPC 117 506 0 20 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
On the optimality of shape and data representation in the spectral domain Y. Aflalo H. Brezis Ron Kimmel 28 85 0 15 Sep 2014