Point Cloud Segmentation Algorithm Based on Contrastive Learning and Label Mining

Huang Hua; Bu Yifan; Xu Hongli; Wang Xiaorong

doi:10.7544/issn1000-1239.202330491

Journal of Computer Research and Development > 2025 > 62(1): 132-143. > DOI: 10.7544/issn1000-1239.202330491 CSTR: 32373.14.issn1000-1239.202330491

Huang Hua, Bu Yifan, Xu Hongli, Wang Xiaorong. Point Cloud Segmentation Algorithm Based on Contrastive Learning and Label Mining[J]. Journal of Computer Research and Development, 2025, 62(1): 132-143. DOI: 10.7544/issn1000-1239.202330491

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Point Cloud Segmentation Algorithm Based on Contrastive Learning and Label Mining

Huang Hua^{1, 2, 4,},
Bu Yifan^{1, 2},
Xu Hongli^{1, 3},
Wang Xiaorong^{1, 2}

1.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
2.
Beijing Key Lab of Traffic Data Analysis and Mining (Beijing Jiaotong University), Beijing 100044
3.
Key Laboratory of Beijing for Railway Engineering (Beijing Jiaotong University), Beijing 100044
4.
Frontiers Science Center for Smart High-speed Railway System (Beijing Jiaotong University), Beijing 100044

Funds: This work was supported by the National Natural Science Foundation of China (51827813), the National Key Research and Development Program of China (2022YFB2603302), the Research & Development Program of Beijing Municipal Education Commission (KJZD20191000402) , and the Fundamental Research Funds for the Central Universities (2022JBQY009).

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Author Bio:
Huang Hua: born in 1977. PhD, associate professor. Member of CCF. His main research interests include computer vision, machine learning, and the applications in the railway field

Bu Yifan: born in 1998. Master. Her main research interests include 3D vision and point cloud segmentation

Xu Hongli: born in 1963. PhD, professor. Her main research interests include machine learning and machine vision

Wang Xiaorong: born in 1999. Master candidate. Student member of CCF. His main research interests include 3D vision and point cloud segmentation
Received Date: June 11, 2023
Revised Date: February 05, 2024
Available Online: November 12, 2024

Graphical Abstract

Abstract

Abstract

Point cloud segmentation algorithm based on deep learning can effectively segment point clouds in high-dimensional space by designing complex feature extraction modules. However, the lack of feature mining for boundary point set results in suboptimal accuracy in boundary segmentation. Some studies have applied the idea of contrastive learning to point cloud segmentation to solve the problem of insufficient boundary region segmentation performance, but the disorder and sparse characteristics of point cloud have not been fully utilized, and the feature extraction is not accurate enough. To solve these problems, we propose CL2M to learn more accurate features of point clouds at different locations through the self-attention mechanism, and the contrastive learning method is introduced to improve the segmentation accuracy of point cloud boundaries. In the process of contrastive boundary learning, labels in semantic space are deeply mined and a contrastive boundary learning module based on label distribution is designed to make the label distribution of point cloud in high-dimensional space contain more semantic information. The model makes full use of the label distribution law to calculate the distance between distributions, and can accurately divide positive and negative samples, reducing the cumulative errors caused by conventional hard partition. The results on two public data sets show that CL2M is superior to the existing point cloud segmentation model on several evaluation indexes, which verifies the effectiveness of the model.
- computer vision,
- point cloud segmentation,
- contrastive learning,
- self-attention mechanism,
- boundary mining

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References (40)

References

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