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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (4): 755-763.DOI: 10.11996/JG.j.2095-302X.2023040755

• Computer Graphics and Virtual Reality • Previous Articles     Next Articles

Geometric feature guided multi-level segmentation for object point clouds

LIU Yan(), XIONG You-yi, HAN Miao-miao, YANG Long()   

  1. College of Information Engineering, Northwest A&F University, Yangling Shaanxi 712100, China
  • Received:2022-12-05 Accepted:2023-03-01 Online:2023-08-31 Published:2023-08-16
  • Contact: YANG Long (1982-), associate professor, Ph.D. His main research interest covers computer graphics. E-mail:yl@nwafu.edu.cn
  • About author:

    LIU Yan (1997-), master student. Her main research interest covers computer graphics. E-mail:yanl@nwafu.edu.cn

  • Supported by:
    National Natural Science Foundation of China(61702422);Natural Science Basic Research Program of Shaanxi(2022JM-378)


The proliferation of scanning object point clouds has brought surface shape analysis to the forefront of research in the computer graphics community. Tasks such as structure extraction, shape editing, and human-object interaction necessitate as precise a point cloud part segmentation as possible. However, due to the complexities of shallow geometric feature extraction, shape loss, and noise interference, the part segmentation, especially the small instance part extraction for scanning point clouds, remains relatively difficult. To address this issue, a multi-level part instance segmentation method guided by geometric features was proposed for object point clouds. The concave, convex, and boundary features were extracted based on the local bending extent of the point cloud surface. Firstly, our method segmented the general structure along the most global prominent concave line. Then it subdivided those segments with obvious geometric differences according to the local shallow features. Finally, the method performed concave-convex collaborative segmentation for some segments to obtain the multi-level segmentation results. The three-stage segmentation process from coarse to fine, along the geometric features from deep to shallow, allowed for better consideration of parts with different scales and finer grained part instance segmentation. The experimental results demonstrated that the proposed method could achieve superior segmentation results on both scanning point clouds and sampled point clouds from CAD models. It provided a more precise and effective method for part segmentation of scanning object point clouds.

Key words: point cloud model, 3D surface segmentation, feature detection, convexity-concavity, region growing

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