SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid
Shape Correspondence

SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence

16 September 2022

Souhaib Attaiki

Papers citing "SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence"

9 / 9 papers shown

Title
Wormhole Loss for Partial Shape Matching Amit Bracha Thomas Dagès Ron Kimmel 24 2 0 30 Oct 2024
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction Souhaib Attaiki M. Ovsjanikov 3DPC 29 2 0 11 Mar 2024
Unsupervised Representation Learning for Diverse Deformable Shape Collections Sara Hahner Souhaib Attaiki Jochen Garcke M. Ovsjanikov 15 1 0 27 Oct 2023
Zero-Shot 3D Shape Correspondence Ahmed Abdelreheem Abdelrahman Eldesokey M. Ovsjanikov Peter Wonka 28 24 0 05 Jun 2023
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching Lei Li Nicolas Donati M. Ovsjanikov 21 21 0 12 Oct 2022
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding Saining Xie Jiatao Gu Demi Guo C. Qi Leonidas J. Guibas Or Litany 3DPC 134 618 0 21 Jul 2020
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features Xuyang Bai Zixin Luo Lei Zhou Hongbo Fu Long Quan Chiew-Lan Tai 3DPC 31 375 0 06 Mar 2020
3D-CODED : 3D Correspondences by Deep Deformation Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell Mathieu Aubry 3DPC 3DV 115 325 0 13 Jun 2018
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 228 3,202 0 24 Nov 2016