Geometric Feedback Network for Point Cloud Classification
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As the basic task of point cloud learning, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network designed as a feedback mechanism, a procedure allowing the modification of the output via as a response to the output, to comprehensively capture the local features of 3D point clouds. Besides, we also enrich the explicit and implicit geometric information of point clouds in low-level 3D space and high-level feature space, respectively. By applying an attention module based on channel affinity, that focuses on distinct channels, the learned feature map of our network can effectively avoid redundancy. The performances on synthetic and real-world datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.
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