A multi-feature fusion probabilistic topic model for unsupervised 3D point cloud classification

Jun Chen; Fan Xu; Zhigao Shang; Shuning Shao

doi:10.1117/12.2653513

18 November 2022 A multi-feature fusion probabilistic topic model for unsupervised 3D point cloud classification

Jun Chen, Fan Xu, Zhigao Shang, Shuning Shao

Proceedings Volume 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022); 124731M (2022) https://doi.org/10.1117/12.2653513
Event: Second International Conference on Optics and Communication Technology (ICOCT 2022), 2022, Hefei, China

Abstract

In this paper, a multi-feature fusion probabilistic topic model, called MFF-PTM, is proposed to realize unsupervised 3D point cloud classification. Our MFF-PTM consists of three key stages: 1) a novel multi-feature descriptor is designed to characterize different 3D point clouds by the combination of statistical, morphological and histogram features; 2) a Rsphere clustering algorithm is proposed to construct 3D visual vocabulary and generate the co-occurrence matrix, which can effectively avoid the initialization problem of category; 3) PTM employs the co-occurrence matrix to predict the probability distribution of a certain point cloud belonging to different category topics. The experimental results have clearly shown that the proposed MFF-PTM model can outperform the traditional PTM models with single feature description for 3D point cloud classification.

Citation Download Citation

Jun Chen, Fan Xu, Zhigao Shang, and Shuning Shao "A multi-feature fusion probabilistic topic model for unsupervised 3D point cloud classification", Proc. SPIE 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022), 124731M (18 November 2022); https://doi.org/10.1117/12.2653513

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