CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for
3D Point Clouds

CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds

31 December 2020

Hui Yuan

Papers citing "CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds"

6 / 6 papers shown

Title
Denoising Functional Maps: Diffusion Models for Shape Correspondence Aleksei Zhuravlev Zorah Lähner Vladislav Golyanik DiffM 63 1 0 03 Mar 2025
Sync4D: Video Guided Controllable Dynamics for Physics-Based 4D Generation Zhoujie Fu Jiacheng Wei Wenhao Shen Chaoyue Song Xiaofeng Yang Fayao Liu Xulei Yang Guosheng Lin 3DGS 25 5 0 27 May 2024
Non-Rigid Shape Registration via Deep Functional Maps Prior Puhua Jiang Mingze Sun Ruqi Huang 11 10 0 08 Nov 2023
Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds Zhiyuan Zhang Yuchao Dai Bin Fan Jiadai Sun Mingyi He 3DPC 30 6 0 26 Oct 2022
3D-CODED : 3D Correspondences by Deep Deformation Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell Mathieu Aubry 3DPC 3DV 107 305 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