基于对称先验与协同训练多视图子空间聚类的点云部件分割方法

doi:10.11896/jsjkx.260600097

Abstract

Abstract: To address the issues of high computational complexity and information redundancy caused by multi-dimensional feature fusion in 3D point cloud part segmentation,a point cloud part segmentation method based on co-training multi-view subspace clustering is proposed.Firstly,based on superpoint over-segmentation,an affinity graph optimization strategy with symmetry constraints is proposed,which accurately captures the physical structure boundaries of objects while significantly reducing the computational complexity of subsequent clustering.Secondly,targeting the structural characteristics of 3D parts,a multi-feature representation encompassing spatial position,robust representative normals,and customized multi-dimensional geometric attributes is constructed for superpoints.Subsequently,in the co-clustering stage,a co-training multi-view subspace clustering framework is utilized to perform eigendecomposition on the kernel matrix to obtain a robust data representation with low redundancy.This representation learning and the subspace clustering are then alternately optimized to achieve a synergistic evolution of feature dimensionality reduction and clustering accuracy.Finally,a K-nearest neighbor(KNN) majority voting strategy is adopted for the reverse mapping of superpoint labels and boundary smoothing optimization.Experimental results on the ShapeNet and MeshsegBenchmark-1.0 3D point cloud datasets demonstrate that the proposed method effectively overcomes the redundant interference caused by feature fusion and achieves competitive segmentation metrics.Meanwhile,visualization results indicate that the proposed method exhibits highly consistent segmentation performance in parsing local fine structures and preserving complex physical boundaries.The proposed method not only effectively overcomes the computational bottleneck of applying traditional multi-view clustering to large-scale point clouds,but also enhances algorithmic robustness by incorporating heuristic rules,providing a reliable geometry-driven approach for the structural parsing of complex 3D objects.

Key words: Point cloud part segmentation, Multi-view subspace clustering, Superpoint over-segmentation, Symmetry prior, Shape-Net

CLC Number:

TP391.41

DAI Yeqing, WANG Chunxue, DENG Chongyang. 3D Point Cloud Part Segmentation via Symmetry Prior and Co-training Multiview Subspace Clustering[J].Computer Science, 2026, 53(9): 249-260.

References

[1] RABBANI T,VAN D H.Segmentation of point clouds usingsmoothness constraint [J].International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences,2006,36(5):248-253.
[2] FISCHLER M A,BOLLES R C.Random Sample Consensus:A paradigm for model fitting withapplications to image analysis and automated cartography [M] //Readings in Computer Vision:Issues,Problems,Principles,and Paradigms.Los Altos:Morgan Kaufmann Publishers,1987:381-395.
[3] ULRIKE V L.A tutorial on spectralclustering [J].Statisticsand Computing,2007,17(4):395-416.
[4] SHI J,MALIK J.Normalized cuts and image segmentation[C] //Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2026:731-737.
[5] PAPON J,ABRAMOV A,SCHOELER M,et al.Voxel cloudconnectivity segmentation-supervoxels for point clouds [C] //2013 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2013:2027-2034.
[6] LANDRIEU L,SIMONOVSKY M.Large-scale point cloud semantic segmentation with superpoint graphs [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2018:3298-3307.
[7] MATURANA D,SCHERER S.VoxNet:A 3D convolutionalneural network for real-time object recognition [C] //2015 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2015:922-928.
[8] SU H,MAJI S,KALOGERAKIS E,et al.Multi-view convolu-tional neural networks for 3D shape recognition [C] //2015 IEEE International Conference on Computer Vision(ICCV).IEEE,2015:945-953.
[9] BOULCH A,LE SAUX B,AUDEBERT N.Unstructured point cloud semantic labeling using deep segmentation networks [C] //Proceedings of the Workshop on 3D Object Retrieval(3DOR).2017.
[10] KLOKOV R,LEMPITSKY V.Escape from cells:Deep kd-networks for the recognition of 3d point cloud models [C] //Proceedings of the IEEE International Conference on Computer Vision.2017:863-872.
[11] QI C R,SU H,KAICHUN M,et al.PointNet:deep learning on point sets for 3D Classification and segmentation [C] //2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2017:77-85.
[12] QI C R,YI L,SU H,et al.PointNet++:deep hierarchical feature learning on point sets in a metric space [C] //Advances in Neural Information Processing Systems.2017:5099-5108.
[13] THOMAS H,QI C R,DESCHAUD J E,et al.KPConv:flexible and deformable convolution for point clouds [C] //2019 IEEE/CVF International Conference on Computer Vision(ICCV).IEEE,2019:6410-6419.
[14] LI Y,BU R,SUN M,et al.PointCNN:Convolution on X-transformed points [C] //Advances in Neural Information Processing Systems.2018:820-830.
[15] WU W,QI Z,FUXIN L.PointConv:Deep convolutional net-works on 3D point clouds [C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:9621-9630.
[16] YI L,SU H,GUO X,et al.SyncSpecCNN:Synchronized spectral CNN for 3D shape segmentation [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2282-2290.
[17] WANG Y,SUN Y,LIU Z,et al.Dynamic graph cnn for learning on point clouds [J].ACM Transactions on Graphics,2019,38(5):1-12.
[18] ZHAO H,JIANG L,JIA J.Point transformer [C] //Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:16259-16268.
[19] ZHOU Y,GU J,CHIANG T Y,et al.Point-SAM:promptable 3D segmentation model for point clouds [C] //International Conference on Learning Representations.2025.
[20] MO K,ZHU S,CHANG A X,et al.PartNet:A large-scalebenchmark for fine-grained and hierarchical part-level 3D object understanding [C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:909-918.
[21] CAO X,ZHANG C,FU H,et al.Diversity-induced multi-view subspace clustering [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:586-594.
[22] ZHANG C,HU Q,FU H,et al.Latent multi-view subspace clustering [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4279-4287.
[23] ELHAMIFAR E,VIDAL R.Sparse subspace clustering:algorithm,theory,and applications [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2765-2781.
[24] HU R,FAN L,LIU L.Co-segmentation of 3D shapes via subspace clustering [J].Computer Graphics Forum,2012,31(5):1703-1713.
[25] CHEN Z,YIN K,FISHER M,et al.BAE-NET:Branched au-toencoder for shape co-segmentation [C] //Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:7026-7035.
[26] LIU J,LIU X,YANG Y,et al.Multiview subspace clustering via co-training robust data representation [J].IEEE Transactions on Neural Networks and Learning Systems,2022,33(10):5177-5189.
[27] RUSU R B,COUSINS S.3D is here:point cloud library(PCL) [C] //2011 IEEE International Conference on Robotics and Automation.IEEE,2011:1-4.
[28] HOPPE H,DEROSE T,DUCHAMP T,et al.Surface recon-struction from unorganized points [M] //Seminal Graphics Papers:Pushing the Boundaries,Volume 2.New York:ACM,2023:95-102.
[29] PAULY M,GROSS M,KOBBELT L P.Efficient simplification of point-sampled surfaces [C] //IEEE Visualization,2002.VIS 2002.IEEE,2002:163-170.
[30] ANDREW Y N,MICHAEL I J,YAIR W.On spectral clustering:analysis and an algorithm [C] //Advances in Neural Information Processing Systems.2001:849-856.
[31] SIDI O,VAN KAICK O,KLEIMAN Y,et al.Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering [J].ACM Transactions on Graphics,2011,30(6):1-10.
[32] GOLOVINSKIY A,FUNKHOUSER T.Consistent segmenta-tion of 3D models [J].Computers & Graphics,2009,33(3):262-269.
[33] GU S,ZHANG L,ZUO W,et al.Weighted nuclear norm minimization with application to image denoising[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:2862-2869.
[34] LU X,SCHAEFER S,LUO J,et al.Low rank matrix approximation for 3D geometry filtering [J].IEEE Transactions on Visualization and Computer Graphics,2022,28(4):1835-1847.
[35] PATEL V M,VIDAL R.Kernel sparse subspace clustering[C] //2014 IEEE International Conference on Image Proces-sing.IEEE,2014:2849-2853.
[36] SCHÖLKOPF B,SMOLA A J.Learning with kernels:support vector machines,regularization,optimization,and beyond [M].MIT Press,2002.
[37] ZELNIK-MANOR L,PERONA P.Self-tuning spectral clustering [C] //Advances in Neural Information Processing Systems.2004:1601-1608.
[38] CHANG A X,FUNKHOUSER T,GUIBAS L,et al.ShapeNet:An information-rich 3D model repository [J].arXiv:1512.03012,2015.
[39] CHEN X,GOLOVINSKIY A,FUNKHOUSER T.A bench-mark for 3D mesh segmentation [C] //ACM SIGGRAPH 2009 Papers.New York:ACM,2009:1-12.
[40] WU Z,SHOU R,WANG Y,et al.Interactive shape co-segmentation via label propagation [J].Computers & Graphics,2014,38:248-254.
[41] YI L,KIM V G,CEYLAN D,et al.A scalable active framework for region annotation in 3D shape collections [J].ACM Transactions on Graphics,2016,35(6):1-12.

Related Articles 15

[1]	LIU Xinlin, WU Keqiang, XU Chang, LI Xiao. Research on Collaborative Ranging Method for Moving Targets Based on Decentralized Multi-view Visual Baseline [J]. Computer Science, 2026, 53(9): 180-187.
[2]	HE Yuhuang, WANG Qianlei DAI Yuntong DU Wu, QIN Xiaolin. Six Degrees of Freedom Pose Estimation Method Fusing Spectral and Geometric Features [J]. Computer Science, 2026, 53(9): 209-218.
[3]	WANG Pengfei, GUO Qinghao, ZENG Qiong, CHEN Shuangmin, XIN Shiqing , TU Changhe. Efficient Approximate Point-to-Mesh Distance Query via Sampling Proxies and Dynamic Programming [J]. Computer Science, 2026, 53(9): 240-248.
[4]	HU Changyu, FAN Xinyu, ZHANG Zhi, DING Zixu, ZHANG Zhengyue, PENG Juhong. Hierarchical Lightweight Micro-expression Recognition Based on Optical Flow Partitioned FeatureFusion [J]. Computer Science, 2026, 53(8): 174-181.
[5]	WANG Yifan, YANG Peixuan, LU Yuansuo, LIU Mengjun. Teacher Trajectory Recognition and Reconstruction in Smart Classrooms via Multi-source Fusion [J]. Computer Science, 2026, 53(8): 191-200.
[6]	CHEN Yifan, DING Cong, CAO Min. Image Anomaly Detection Based on Masked Convolutional Kernel [J]. Computer Science, 2026, 53(7): 45-53.
[7]	JIAO Hanbing, KANG Junhua, XIAO Teng, DENG Fei. Low-light Image Enhancement Network Based on Multi-level Illumination Excitation and JointLoss Constraint [J]. Computer Science, 2026, 53(7): 62-70.
[8]	CHU Chunyu, JIANG Feilong. Water Meter Reading Recognition Based on Deep Learning and Prior Correction [J]. Computer Science, 2026, 53(6A): 250300143-7.
[9]	LIU Dai, AN Pengyu, WANG Kai. Improved YOLOv5s-based Algorithm for Emergency Situation Detection in Airport Terminals [J]. Computer Science, 2026, 53(6A): 250300174-7.
[10]	WANG Sheng, ZHANG Linghao, ZHANG Juling, PANG Bo, XI Ning, SHE Wenkui. Infrared and Visible Image Homography Estimation for Power Equipment Based on Improved MobileNetV4 [J]. Computer Science, 2026, 53(6A): 250400077-7.
[11]	SHAN Chengcheng, LI Weiting, MEI Chun, ZHAO Hui, QIAN Weixing, ZENG Qinghua. Asynchronous Dynamic Image Stitching Method Based on Parameter-adaptive Grey WolfOptimization Algorithm [J]. Computer Science, 2026, 53(6A): 250600169-8.
[12]	CHENG Zimeng, YANG Xinyue, AI Haojun, WANG Zhongyuan. Unsupervised Infrared Image Generation Method Based on Dual Semantic Contrastive Learning [J]. Computer Science, 2026, 53(4): 260-268.
[13]	MENG Siyu, NIU Chunxiang, TAN Quange, WANG Rong. Deepfake Detection Method Based on Positional Enhancement and Frequency Domain ComponentInteraction [J]. Computer Science, 2026, 53(4): 445-453.
[14]	XU Lijun, ZHAO Yujie, ZHAO Min, MA Weixuan, CHEN Kansong. Efficient Multi-view Stereo Reconstruction Based on Multi-granularity Feature Aggregation and Binary Search [J]. Computer Science, 2026, 53(3): 257-265.
[15]	LI Ang, ZHANG Jieyuan, LIU Xunyun. Camouflaged Object Detection for Aerial Images Based on Bidirectional Cross-attentionCross-domain Fusion [J]. Computer Science, 2026, 53(1): 173-179.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

3D Point Cloud Part Segmentation via Symmetry Prior and Co-training Multiview Subspace Clustering

PDF (PC)