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Weakly supervised point cloud semantic segmentation via multi-scale local feature fusion
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Jiaying Wu1, 2, 3, Xiaowen Yang1, 2, 3, Xie Han1, 2, 3, Huiyan Han1, 2, 3, Yuan Zhang1, 2, 3, Rong Zhao1, 2, 3
Electronic Measurement Technology | 2026, 49(6) : 167 - 176
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Electronic Measurement Technology | 2026, 49(6): 167-176
• Information Technology and Image Processing •
Weakly supervised point cloud semantic segmentation via multi-scale local feature fusion
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Jiaying Wu1, 2, 3, Xiaowen Yang1, 2, 3, Xie Han1, 2, 3, Huiyan Han1, 2, 3, Yuan Zhang1, 2, 3, Rong Zhao1, 2, 3
Affiliations
  • 1.School of Computer Science and Technology, North University of China, Taiyuan 030051, China
  • 2.Shanxi Key Laboratory of Machine Vision & Virtual Reality, Taiyuan 030051, China
  • 3.Shanxi Vision Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China
doi: 10.19651/j.cnki.emt.2519645
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To address the limitations of existing weakly supervised semantic segmentation models for point clouds, which struggle to balance local feature correlation, generalization, and feature utilization. This paper proposes WS-MLF, a weakly supervised point cloud semantic segmentation model via multi-scale local feature fusion, based on the RAC-Net baseline. Firstly, the raw point cloud data is taken as input, and a multi-scale spherical sampling methods (MSSM) is employed to capture hierarchical features across varying spatial radii. Secondly, a multi-local feature aggregation enhancement module (MFA) is designed to refine geometric context within neighborhoods. Thirdly, a spatial-channel-fused hybrid attention module (SCH-Att) is proposed to prioritize discriminative channels and key points. Finally, a decoder is utilized for upsampling to generate point-level semantic labels, thereby completing the semantic segmentation task. The proposed model is evaluated on large-scale indoor scene datasets, S3DIS and ScanNet-v2. Experimental results demonstrate that on the S3DIS dataset, when the label ratios are 0.02% and 0.06%, the mIoU surpasses RAC-Net by 2.71% and 0.54%, respectively. On the ScanNet-v2 dataset, with a label ratio of 20 pt, the mIoU increases by 1.55% compared with RAC-Net. These results validate WS-MLF's effectiveness in extracting key features under weak supervision, enhancing segmentation accuracy.

weakly supervised  /  point cloud semantic segmentation  /  multi-scale  /  attention mechanism  /  feature aggregation
Jiaying Wu, Xiaowen Yang, Xie Han, Huiyan Han, Yuan Zhang, Rong Zhao. Weakly supervised point cloud semantic segmentation via multi-scale local feature fusion[J]. Electronic Measurement Technology, 2026 , 49 (6) : 167 -176 . DOI: 10.19651/j.cnki.emt.2519645
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Year 2026 volume 49 Issue 6
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doi: 10.19651/j.cnki.emt.2519645
  • Receive Date：2025-08-21
  • Online Date：2026-05-15
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  • Received：2025-08-21
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Affiliations
    1.School of Computer Science and Technology, North University of China, Taiyuan 030051, China
    2.Shanxi Key Laboratory of Machine Vision & Virtual Reality, Taiyuan 030051, China
    3.Shanxi Vision Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China
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https://castjournals.cast.org.cn/joweb/dzcljs/EN/10.19651/j.cnki.emt.2519645
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