A weakly supervised framework for real-world point cloud classification-Reference-Cited by-同舟云学术

A weakly supervised framework for real-world point cloud classification

Published:2022-02 Issue: Volume:102 Page:78-88
ISSN:0097-8493
Container-title:Computers & Graphics
language:en
Short-container-title:Computers & Graphics

Author:

Deng An,Wu Yunchao,Zhang Peng^ORCID,Lu Zhuheng,Li Weiqing,Su Zhiyong^ORCID

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Human-Computer Interaction,General Engineering

Reference62 articles.

1. Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: IEEE conference on computer vision and pattern recognition; 2017. p. 652–60.

2. Xu M, Zhou Z, Qiao Y. Geometry sharing network for 3D point cloud classification and segmentation. In: Proceedings of the thirty-three AAAI conference on artificial intelligence; 2020. p. 12500–07.

3. SHREC 2021: 3D point cloud change detection for street scenes;Ku;Comput Graph,2021

4. Surface reconstruction from unoriented point clouds by a new triangle selection strategy;Wang;Comput Graph,2019

5. Deep learning for 3D point clouds: A survey;Guo;IEEE Trans Pattern Anal Mach Intell,2021

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2. One Class One Click: Quasi scene-level weakly supervised point cloud semantic segmentation with active learning;ISPRS Journal of Photogrammetry and Remote Sensing;2023-10

3. Weakly supervised semantic segmentation of airborne laser scanning point clouds;ISPRS Journal of Photogrammetry and Remote Sensing;2022-05

4. Editorial Note;Computers & Graphics;2022-02