Learning 3D Dense Correspondence via Canonical Point Autoencoder

Learning 3D Dense Correspondence via Canonical Point Autoencoder

10 July 2021

Ming-Hsuan Yang

Papers citing "Learning 3D Dense Correspondence via Canonical Point Autoencoder"

7 / 7 papers shown

Title
Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map Generation Qijin Song Weibang Bai 94 1 0 22 Dec 2024
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features Thomas Wimmer Peter Wonka M. Ovsjanikov 28 9 0 29 Nov 2023
One-Shot Transfer of Affordance Regions? AffCorrs! Denis Hadjivelichkov Sicelukwanda Zwane M. Deisenroth Lourdes Agapito Dimitrios Kanoulas 35 34 0 15 Sep 2022
Autoregressive 3D Shape Generation via Canonical Mapping A. Cheng Xueting Li Sifei Liu Min Sun Ming Yang 3DPC 42 39 0 05 Apr 2022
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence Feng Liu Xiaoming Liu 3DPC 33 36 0 23 Oct 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 14,099 0 02 Dec 2016