PointELM: Fast Point Cloud Classification Using Deep Random Mapping Based Extreme Learning Machines
Zhuangzi Li, Shan Liu, Ge Li · 2024
Designing an influential and instructive deep network has become a prominent research in point cloud analysis. However, deep networks inevitably require extensive training time and are sensitive to variational data distribution. In this paper, we observe that a randomly initialized network exhibits discriminative abilities and show training deep networks is not necessary for point cloud classification. Specifically, we propose a Point Extreme Learning Machine (PointELM), which initially extracts infant features from point clouds using a randomly weighted network. Subsequently, a frequency-domain mapping is designed to enhance the infant features. Finally, an ELM classifier is adopted to categorize the enhanced features and generate the output predictions. We evaluate PointELM on classical networks and highlight three advantages: (1) PointELM does not require backpropagation, enabling extremely fast training. Yet PointELM can still achieve promising performance. For example, the classification accuracy of a DGCNN-based PointELM just lowers a trained DGCNN about 2.8% on ModelNet40. (2) PointELM is a flexible method that can conveniently transfer a trained network to a new dataset with minimal performance loss. (3) PointELM can rapidly alleviate the performance degradation caused by feeding sampled point clouds to trained networks, indicating strong potential on adjusting data with various distributions. Code is available at https://github.com/lizhuangzi/PointELM.