Learning Implicit Functions for Dense 3D Shape Correspondence of Generic
Objects

Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects

29 December 2022

Xiaoming Liu

Papers citing "Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects"

7 / 7 papers shown

Title
Learning Accurate Dense Correspondences and When to Trust Them Prune Truong Martin Danelljan Luc Van Gool Radu Timofte 3DH 3DPC 68 128 0 05 Jan 2021
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration Bharat Lal Bhatnagar C. Sminchisescu Christian Theobalt Gerard Pons-Moll 3DH 89 132 0 23 Oct 2020
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence Feng Liu Xiaoming Liu 3DPC 33 36 0 23 Oct 2020
Convolutional Occupancy Networks Songyou Peng Michael Niemeyer L. Mescheder Marc Pollefeys Andreas Geiger 3DV AI4CE 214 971 0 10 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
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,087 0 02 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015