PointHop: An Explainable Machine Learning Method for Point Cloud Classification

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 at...

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Veröffentlicht in:	IEEE transactions on multimedia 2020-07, Vol.22 (7), p.1744-1755
Hauptverfasser:	Zhang, Min, You, Haoxuan, Kadam, Pranav, Liu, Shan, Kuo, C.-C. Jay
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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