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Parameter optimization criteria guided 3D point cloud classification

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Abstract

3D point cloud classification is one of the basic topics in multimedia analysis and understanding. By the construction of the discriminant model and efficient parameter optimization, point cloud classification can be achieved after the training. However, most parameter optimization methods do not guarantee the highest global classification accuracy with a high classification accuracy on smaller classes. In addition, geometric features of the point cloud are not sufficiently utilized. In this paper, we use local geometric shape features including the nearest neighbor tetrahedral volume, Gaussian curvature, the neighbourhood normal vector consistency and the neighbourhood minimum principal curvature direction consistency. We propose three discrete criteria for parameter optimization to design explicit functions, and we present concrete algorithms, in which Monte Carlo method and Probabilistic Neural Network method are employed to estimate these parameters respectively. Experimental results show that our criteria can be applied to the classification of the 3D point cloud of the scene, and can be used to improve the classification accuracy of small-scale point sets when different classes have great disparities in the number.

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Acknowledgements

This work is partly supported by the Fundamental Research Funds for the Central Universities(NO.2015ZCQ-LY-01), and partly supported by National Natural Science Foundation of China with Nos. 61372190, 61571439, 61561003, 61502490 and 61501464, and partly supported by Project 6140001010207.

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Authors and Affiliations

  1. College of Science, Beijing Forestry University, Beijing, China

    Hongjun Li

  2. LIAMA - NLPR, CAS Institute of Automation, Beijing, China

    Weiliang Meng, Xinying Liu, Shiming Xiang & Xiaopeng Zhang

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  1. Hongjun Li
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  2. Weiliang Meng
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  3. Xinying Liu
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  4. Shiming Xiang
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  5. Xiaopeng Zhang
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Correspondence to Hongjun Li.

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Li, H., Meng, W., Liu, X. et al. Parameter optimization criteria guided 3D point cloud classification. Multimed Tools Appl 78, 5081–5104 (2019). https://doi.org/10.1007/s11042-018-6838-z

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