PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

• URL: http://arxiv.org/abs/2002.03281v2
• Date: Sat, 23 May 2020 03:56:54 GMT
• Title: PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
• Authors: Min Zhang, Yifan Wang, Pranav Kadam, Shan Liu and C.-C. Jay Kuo
• Abstract summary: The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. We improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.
• Score: 55.887502438160304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.

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