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  • Received: Feb. 2, 2020

    Accepted: Mar. 9, 2020

    Posted: Oct. 1, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Yang Jun (yangj@mail.lzjtu.cn)

    DOI: 10.3788/LOP57.201510

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    Jun Yang, Donghao Li. Co-Segmentation of 3D Model Clusters Based on Point Cloud Sparse Coding[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201510

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Laser & Optoelectronics Progress, Vol. 57, Issue 20, 201510 (2020)

Co-Segmentation of 3D Model Clusters Based on Point Cloud Sparse Coding

Yang Jun1,* and Li Donghao2

Author Affiliations

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • 2School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

Abstract

Aim

ing at the problems of the co-analysis of multiple 3D models in the function space and the co-segmentation of the whole model cluster, we propose a co-segmentation method based on point cloud sparse coding. First, the point cloud feature is extracted and the 3D information is transformed into the feature space. Second, the dictionary matrix and sparse vectors are constructed after the decomposition of the feature vectors into the base vectors by the deep learning network. Finally, the test data is represented by dictionary sparseness and the category of each point in the point cloud model is determined. To get the co-segmentation result, the homogeneous points are divided into the same region. The experimental results show that the segmentation accuracy on ShapeNet Parts dataset obtained using the proposed algorithm is 85.7%. Compared to the current mainstream algorithms used for segmentation, the proposed algorithm can not only compute the relational structure of model clusters more effectively, but also improve the segmentation accuracy and effect.

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