Fully Understanding Generic Objects: Modeling, Segmentation, and
Reconstruction

Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

2 April 2021

Xiaoming Liu

Papers citing "Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction"

9 / 9 papers shown

Title
High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity Qian Yu Peng-Tao Jiang Hao Zhang Jinwei Chen Bo Li Lihe Zhang Huchuan Lu 44 2 0 14 Oct 2024
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification Feng Liu Minchul Kim ZiAng Gu Anil Jain Xiaoming Liu 3DH 41 27 0 21 Aug 2023
Neural Voting Field for Camera-Space 3D Hand Pose Estimation Lin Huang Chung-Ching Lin Kevin Qinghong Lin Lin Liang Lijuan Wang Junsong Yuan Zicheng Liu 29 4 0 07 May 2023
Neural Correspondence Field for Object Pose Estimation Lin Huang Tomás Hodan Lingni Ma Linguang Zhang Luan Tran Christopher D. Twigg Po-Chen Wu Junsong Yuan Cem Keskin Robert Y. Wang 3DH 26 24 0 30 Jul 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
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence Feng Liu Xiaoming Liu 3DPC 35 36 0 23 Oct 2020
Shape and Viewpoint without Keypoints Shubham Goel Angjoo Kanazawa Jitendra Malik 3DV 89 108 0 21 Jul 2020
Convolutional Occupancy Networks Songyou Peng Michael Niemeyer L. Mescheder Marc Pollefeys Andreas Geiger 3DV AI4CE 223 971 0 10 Mar 2020
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