Weakly supervised semantic segmentation for point cloud based on view-based adversarial training and self-attention fusion-Reference-Cited by-同舟云学术

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Weakly supervised semantic segmentation for point cloud based on view-based adversarial training and self-attention fusion

Published:2023-11 Issue: Volume:116 Page:46-54
ISSN:0097-8493
Container-title:Computers & Graphics
language:en
Short-container-title:Computers & Graphics

Author:

Miao Yongwei^ORCID,Ren Guoxiang,Wang Jinrong,Liu Fuchang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Subject

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

Reference30 articles.

1. Geometric processing and shape modeling of 3D point-sampled models;Miao,2014

2. Target-driven visual navigation in indoor scenes using deep reinforcement learning;Zhu,2017

3. Perturbed self-distillation: Weakly supervised large-scale point cloud semantic segmentation;Zhang,2021

4. Point cloud augmentation with weighted local transformations;Kim,2021

5. PointAugment: An auto-augmentation framework for point cloud classification;Li,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Weakly Supervised Point Cloud Semantic Segmentation via Dynamic Label Propagation and Pseudo-Label Optimization;2024 International Symposium on Intelligent Robotics and Systems (ISoIRS);2024-06-14

2. Note Computers & Graphics Issue 116;Computers & Graphics;2023-11

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