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Explainable Machine Learning Methods for Point Cloud Analysis

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3D Point Cloud Analysis

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Abstract

Explainable machine learning methods for point cloud analysis aim to decrease the model and computation complexity of current methods while improving their interpretation. These methods are an extension of successive subspace learning (SSL) from 2D images to 3D point clouds. SSL offers a lightweight unsupervised feature learning method based on the inherent statistical properties of data units. The model is significantly smaller than deep neural networks (DNNs) and more computationally efficient. However, it is nontrivial to generalize it to tackle the point cloud analysis problems, because points in a point cloud are irregular and unordered by nature, which is quite different from regular 2D images. In this chapter, we first discuss some early works on SSL processing of 2D images, then illustrate our explainable machine learning methods for point cloud classification, part segmentation, and registration in detail. Finally, we introduce some other applications of SSL .

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Authors and Affiliations

  1. Tencent Media Lab, Palo Alto, CA, USA

    Shan Liu

  2. University of Southern California, Los Angeles, CA, USA

    Min Zhang, Pranav Kadam & C.-C. Jay Kuo

Authors
  1. Shan Liu
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  2. Min Zhang
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  3. Pranav Kadam
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  4. C.-C. Jay Kuo
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Liu, S., Zhang, M., Kadam, P., Kuo, CC.J. (2021). Explainable Machine Learning Methods for Point Cloud Analysis. In: 3D Point Cloud Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-89180-0_4

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