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

[1]	Gong Zheng, Sun Junhua, Zhang Guangjun. Dynamic structured-light measurement for wheel diameter based on the cycloid constraint[J]. Applied Optics, 2016, 55(1): 198−207 doi: 10.1364/AO.55.000198
[2]	秦彩杰,管强. 三维点云数据分割研究现状[J]. 宜宾学院学报,2017,17(6):30−35 doi: 10.3969/j.issn.1671-5365.2017.06.007 Qin Caijie, Guan Qiang. Research status of 3D point cloud data segmentation[J]. Journal of Yibin University, 2017, 17(6): 30−35 (in Chinese) doi: 10.3969/j.issn.1671-5365.2017.06.007
[3]	Bhanu B, Lee S, Ho C C, et al. Range data processing: Representation of surfaces by edges [C] //Proc of the 8th Int Conf on Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 1986: 236−238
[4]	Jiang Xiaoyi, Bunke H. Edge detection in range images based on scan line approximation[J]. Computer Vision and Image Understanding, 1999, 73(2): 183−99 doi: 10.1006/cviu.1998.0715
[5]	Wang Zhe, Hong Liu, Yueliang Qian, et al. Real-time plane segmentation and obstacle detection of 3D point clouds for indoor scenes [C] //Proc of the Computer Vision–ECCV. Berlin: Springer, 2012: 22−31
[6]	Papon J, Alexey A, Markus S, et al. Voxel cloud connectivity segmentation-supervoxels for point clouds [C] //Proc of the 31st IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2013: 2027−2034
[7]	Zhan Qingming, Liang Yu, Liang Yubing. A point cloud segmentation method based on vector estimation and color clustering [C] //Proc of the 2nd Int Conf on Information Science and Engineering. Piscataway, NJ: IEEE, 2010: 3463−3466
[8]	Zhan Qingming, Liang Yu. Segmentation of LiDAR point cloud based on similarity measures in multi-dimension Euclidean space [C] //Proc of the 2nd Advances in Computer Science and Engineering. Berlin: Springer, 2012: 349−357
[9]	Holz D, Holzer S, Rusu R B, et al. Real-Time plane segmentation using RGB-D cameras [C] //Proc of the 15th RoboCup. Berlin: Springer, 2011: 306−317
[10]	Li Yangyan, Wu Xiaokun, Chrysathou Y, et al. Globfit: Consistently fitting primitives by discovering global relations [C/OL] //Proc of the 11th ACM SIGGRAPH. New York: ACM, 2011[2023-01-20]. https://dl.acm.org/doi/abs/10.1145/1964921.1964947
[11]	Awadallah M, Lynn A, Sherin G. Segmentation of sparse noisy point clouds using active contour models [C]//Proc of the 21st IEEE Int Conf on Image Processing (ICIP). Piscataway, NJ: IEEE, 2014: 6061−6065
[12]	Wang Yanmin, Shi Hongbin. A segmentation method for point cloud based on local sample and statistic inference [C] //Proc of the 2nd Int Conf on Geo-Informatics in Resource Management and Sustainable Ecosystem(GRMSE). Berlin: Springer, 2015: 274−282
[13]	Boulch A, Guerry J, Le Saux B, et al. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics, 2018, 71: 189−198
[14]	Wang Wei, Xu Yuan, Ren Yingchao, et al. Parsing of urban facades from 3D point clouds based on a novel multi-view domain[J]. Photogrammetric Engineering & Remote Sensing, 2021, 87(4): 283−293
[15]	Kundu A, Yin X, Fathi A, et al. Virtual multi-view fusion for 3D semantic segmentation [C] //Proc of the 16th European Conf on Computer Vision (ECCV). Berlin: Springer, 2020: 518−535
[16]	Chen Xiaozhi, Ma Huimin, Wan Ji, et al. Multi-view 3D object detection network for autonomous driving [C] //Proc of the 35th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2017: 1907−1915
[17]	Qi C R, Su Hao, Mo K, et al. PointNet: Deep learning on point sets for 3D classification and segmentation [C] //Proc of the 35th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2017: 652−660
[18]	Qi C R, Yi L, Su Hao, et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space[J/OL]Advances in Neural Information Processing Systems, 2017, 30. [2024-01-20]. https://proceedings.neurips.cc/paper_files/paper/2017/hash/d8bf84be3800d12f74d8b05e9b89836f-Abstract.html
[19]	Thomas H, Qi C R, Deschaud J E, et al. KPConv: Flexible and deformable convolution for point clouds [C] //Proc of the 37th IEEE/CVF Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2019: 6411−6420
[20]	Xu Mutian, Ding Runyu, Zhao Hengshuang, et al. PAConv: Position adaptive convolution with dynamic kernel assembling on point clouds [C] //Proc of the 39th IEEE/CVF Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2021: 3173−3182
[21]	Wu Felix, Fan A, Baevski A, et al. Pay less attention with lightweight and dynamic convolutions [J]. arXiv preprint, arXiv: 1901.10430, 2019
[22]	Yang Zhilin, Dai Zihang, Yang Yiming, et al. XLNet: Generalized autoregressive pretraining for language understanding[J]. Neural Information Processing Systems, 2019, 33: 5753−5763
[23]	Devlin J, Chang Ming-Wei, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding [J]. arXiv preprint, arXiv: 1810.04805, 2018
[24]	Zhao Hengshuang, Li Jiang, Jia Jiaya, et al. Point Transformer [C] //Proc of the 39th IEEE/CVF Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2021: 16259−16268
[25]	Chen Ting, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations [C] //Proc of the 37th Int Conf on Machine Learning. Washington, DC: ICLR, 2020: 1597−1607
[26]	Khosla P, Teterwak P, Wang Chen, et al. Supervised contrastive learning[J]. Neural Information Processing Systems, 2020, 33: 18661−18673
[27]	Xu Yufei, Zhang Qiming, Zhang Jing, et al. RegionCL: Can simple region swapping contribute to contrastive learning[J]. arXiv preprint, arXiv: 2111.12309, 2021
[28]	Tang Liyao, Zhan Yibing, Chen Zhe, et al. Contrastive boundary learning for point cloud segmentation[C]//Proc of the 40th IEEE/CVF Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2022: 8489−8499
[29]	Oord A, Li Yazhe, Vinyals O. Representation learning with contrastive predictive coding[J]. arXiv preprint, arXiv: 1807.03748, 2018
[30]	张重生,陈杰,李岐龙,等. 深度对比学习综述[J]. 自动化学报,2023,49(1):15−39 Zhang Chongsheng, Chen Jie, Li Qilong, et al. A review of deep contrast learning[J]. Acta Automatica Sinica, 2023, 49(1): 15−39 (in Chinese)
[31]	Hou Sikang, Shi Hongye, Cao Xianghai, et al. Hyperspectral imagery classification based on contrastive learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1−13
[32]	Wang Xinlong, Zhang Rufeng, Shen Chunhua, et al. Dense contrastive learning for self-supervised visual pre-training[C] //Proc of the 39th IEEE/CVF Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2021: 3024−3033
[33]	Chaitanya K, Erdil E, Karani N, et al. Contrastive learning of global and local features for medical image segmentation with limited annotations[J]. Advances in Neural Information Processing Systems, 2020, 33: 12546−12558
[34]	康健,王智睿,祝若鑫,等. 基于监督对比学习正则化的高分辨率 SAR 图像建筑物提取方法[J]. 雷达学报,2022,11(1):157−167 doi: 10.12000/JR21124 Kang Jian, Wang Zhirui, Zhu Ruoxin, et al. High-resolution SAR image building extraction method based on supervised contrast learning regularization[J]. Journal of Radar, 2022, 11(1): 157−167 (in Chinese) doi: 10.12000/JR21124
[35]	侯琳琳. 基于几何特征的三维点云分割算法研究[D]. 北京:北京交通大学,2019 Hou Linlin. Research on 3D point cloud segmentation algorithm based on geometric features [D]. Beijing: Beijing Jiaotong University, 2019 (in Chinese)
[36]	李娇娇,孙红岩,董雨,等. 基于深度学习的3维点云处理综述[J]. 计算机研究与发展,2022,59(5):1160−1179 doi: 10.7544/issn1000-1239.20210131 Li Jiaojiao, Sun Hongyan, Dong Yu, et al. Survey of 3-dimensional point cloud processing based on deep learning[J]. Journal of Computer Research and Development, 2022, 59(5): 1160−1179 (in Chinese) doi: 10.7544/issn1000-1239.20210131
[37]	Wu Wenxuan, Qi Zhongang, Li Fuxi. PointConv: Deep convolutional networks on 3D point clouds[C]//Proc of the 39th IEEE/CVF Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2019: 9621−9630
[38]	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation[C] //Proc of the 18th Medical Image Computing and Computer-Assisted Intervention (MICCAI). Berlin: Springer, 2015: 234−241
[39]	Armeni I, Sax S, Zamir A R, et al. Joint 2D-3D-semantic data for indoor scene understanding[J]. arXiv preprint, arXiv: 1702.01105, 2017
[40]	Yi Li, Shao Lin, Manolis S, et al. Large-scale 3D shape reconstruction and segmentation from shapenet core55[J]. arXiv preprint, arXiv: 1710.06104, 2017

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