Explainable Machine Learning Methods for Point Cloud Analysis

Shan Liu, Min Zhang, Pranav Kadam and C.-C. Jay Kuo
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Shan Liu: Tencent Media Lab
Min Zhang: University of Southern California
Pranav Kadam: University of Southern California
C.-C. Jay Kuo: University of Southern California

Chapter Chapter 4 in 3D Point Cloud Analysis, 2021, pp 87-140 from Springer

Abstract: 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)Successive subspace learning (SSL) from 2D images to 3D point clouds. SSL Successive subspace learning (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) 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 Successive subspace learning (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 Successive subspace learning (SSL) .

Date: 2021
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DOI: 10.1007/978-3-030-89180-0_4

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