Apm: Adaptive Permutation Module For Point Cloud Classification

Despite deep convolutional networks have been highly successful in 2D vision tasks, extending them to point cloud classification task is still challenging due to irregularities of point cloud data relative to image grids. Recent methods usually take advantage of symmetric operators like max-pooling, to deal with point cloud order ambiguity. However, this kind of treatment does not consider the latent geometric informa-tion contained in the spatial order, which may limit the performance of feature learning. To address this issue, we present an adaptive permutation module (APM) that calculates a particular permutation from the input point clouds to achieve permutation invariance, as demonstrated by the visualization of the APM output feature maps. Thorough experiments are conducted to demonstrate the superiority of APM as well. In addition, the APM can be plugged into other state-of-the-art approaches flexibly to further improve performance in classification task. We build an end-to-end deep convolutional neural network applying PointCNN as our backbone combined with the adaptive permutation module and achieve state -of-the-art performance in point cloud classification task. Our work demonstrates that the latent spatially-local correlations play a critical role in feature learning on point clouds.(c) 2021 Elsevier Ltd. All rights reserved.