Towards 3D Scene Understanding by Referring Synthetic Models

Towards 3D Scene Understanding by Referring Synthetic Models

20 March 2022

Xinge Zhu

Yuexin Ma

Wenping Wang

Papers citing "Towards 3D Scene Understanding by Referring Synthetic Models"

8 / 8 papers shown

Title
Zero-shot point cloud segmentation by transferring geometric primitives Runnan Chen Xinge Zhu Nenglun Chen Wei Li Yuexin Ma Ruigang Yang Wenping Wang 3DPC 19 8 0 18 Oct 2022
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation Li Jiang Shaoshuai Shi Zhuotao Tian Xin Lai Shu-Lin Liu Chi-Wing Fu Jiaya Jia SSL 3DPC 118 115 0 15 Oct 2021
Mix3D: Out-of-Context Data Augmentation for 3D Scenes Alexey Nekrasov Jonas Schult Or Litany Bastian Leibe Francis Engelmann 3DPC 142 154 0 05 Oct 2021
Hierarchical Aggregation for 3D Instance Segmentation Shaoyu Chen Jiemin Fang Qian Zhang Wenyu Liu Xinggang Wang 3DPC 47 162 0 05 Aug 2021
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds Yiming Zeng Y. Qian Zhiyu Zhu Junhui Hou Hui Yuan Ying He 3DPC 100 66 0 31 Dec 2020
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding Saining Xie Jiatao Gu Demi Guo C. Qi Leonidas J. Guibas Or Litany 3DPC 139 620 0 21 Jul 2020
Joint 2D-3D-Semantic Data for Indoor Scene Understanding Iro Armeni S. Sax Amir Zamir Silvio Savarese 3DV 3DPC 113 873 0 03 Feb 2017
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 219 14,047 0 02 Dec 2016