Learning Implicit Functions for Topology-Varying Dense 3D Shape
Correspondence

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

23 October 2020

Xiaoming Liu

Papers citing "Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence"

10 / 10 papers shown

Title
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features Thomas Wimmer Peter Wonka M. Ovsjanikov 36 9 0 29 Nov 2023
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision Jiahui Lei Congyue Deng Karl Schmeckpeper Leonidas J. Guibas Kostas Daniilidis 3DPC 24 21 0 27 Mar 2023
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images Vishal Vinod Tanmay Shah Dmitry Lagun 35 4 0 24 Mar 2023
Laplacian ICP for Progressive Registration of 3D Human Head Meshes Nick E. Pears H. Dai William A. P. Smith Haobo Sun 3DH 14 3 0 04 Feb 2023
Correspondence Distillation from NeRF-based GAN Yushi Lan Chen Change Loy Bo Dai 25 9 0 19 Dec 2022
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations Mingwu Zheng Hongyu Yang Di Huang Liming Luke Chen 3DH 36 59 0 28 Mar 2022
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image Feng Liu Xiaoming Liu 3DPC 22 30 0 04 Nov 2021
i3DMM: Deep Implicit 3D Morphable Model of Human Heads Tarun Yenamandra A. Tewari Florian Bernard Hans-Peter Seidel Mohamed A. Elgharib Daniel Cremers Christian Theobalt 3DH 23 121 0 28 Nov 2020
Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence Yu Deng Jiaolong Yang Xin Tong 19 149 0 27 Nov 2020
3D-CODED : 3D Correspondences by Deep Deformation Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell Mathieu Aubry 3DPC 3DV 117 325 0 13 Jun 2018