PointHop: An Explainable Machine Learning Method for Point Cloud Classification

• Published in: IEEE transactions on multimedia 2020-07, Vol.22 (7), p.1744-1755
• Main Authors: Zhang, Min, You, Haoxuan, Kadam, Pranav, Liu, Shan, Kuo, C.-C. Jay
• Format: Article
• Language: English
• Subjects: 3D object recognition; Buildings; Classification; Computational modeling; computer vision; Data exchange; Deep learning; Explainable machine learning; Feature extraction; Machine learning; point cloud cla-ssification; Saab transform; Three-dimensional displays; Training; Transforms
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2019.2963592

An explainable machine learning method for point cloud classification, called the PointHop method, is proposed in this work. The PointHop method consists of two stages: 1) local-to-global attribute building through iterative one-hop information exchange and 2) classification and ensembles. In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit. When we put multiple PointHop units in cascade, the attributes of a point will grow by taking its relationship with one-hop neighbor points into account iteratively. Furthermore, to control the rapid dimension growth of the attribute vector associated with a point, we use the Saab transform to reduce the attribute dimension in each PointHop unit. In the classification and ensemble stage, we feed the feature vector obtained from multiple PointHop units to a classifier. We explore ensemble methods to improve the classification performance furthermore. It is shown by experimental results that the PointHop method offers classification performance that is comparable with state-of-the-art methods while demanding much lower training complexity.