An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation

Author:

Yang Cheng-Kun1,Wu Ji-Jia2,Chen Kai-Syun2,Chuang Yung-Yu1,Lin Yen-Yu2

Affiliation:

1. National Taiwan University

2. National Yang Ming Chiao Tung University

Funder

Ministry of Science and Technology

Publisher

IEEE

Reference49 articles.

1. Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

2. Multi-Path Region Mining for Weakly Supervised 3D Semantic Segmentation on Point Clouds

3. KPConv: Flexible and deformable convolution for point clouds;hugues;CVPR,2019

4. Mining cross-image semantics for weakly supervised semantic segmentation;sun;ECCV,2020

5. 3d spatial recognition without spatially labeled 3d;zhongzheng;CVPR,2021

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1. Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation;Remote Sensing;2024-09-07

2. Advancements in point cloud data augmentation for deep learning: A survey;Pattern Recognition;2024-09

3. DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation;Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24;2024-07-13

4. G-Former: A Grouping Transformer for Weakly Supervised Point Cloud Segmentation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. A lightweight Transformer‐based neural network for large‐scale masonry arch bridge point cloud segmentation;Computer-Aided Civil and Infrastructure Engineering;2024-04-14

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