PAI-Conv: Permutable Anisotropic Convolutional Networks for Learning on Point Clouds
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Demand for efficient representation learning on point clouds is increasing in many 3D computer vision applications. The recent success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to point clouds. However, unlike images that are Euclidean structured, point clouds are irregular since each point's neighbors vary from one to another. Various point neural networks have been developed using isotropic filters or applying weighting matrices to overcome the structure inconsistency on point clouds. However, isotropic filters or weighting matrices limit the representation power. In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters as CNN does. PAI-Conv is physically meaningful and can efficiently cooperate with random point sampling method. Comprehensive experiments demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks compared to state-of-the-art methods.
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