  As the basic task of point cloud analysis, classification is fundamental but
always challenging. To address some unsolved problems of existing methods, we
propose a network that captures geometric features of point clouds for better
representations. To achieve this, on the one hand, we enrich the geometric
information of points in low-level 3D space explicitly. On the other hand, we
apply CNN-based structures in high-level feature spaces to learn local
geometric context implicitly. Specifically, we leverage an idea of
error-correcting feedback structure to capture the local features of point
clouds comprehensively. Furthermore, an attention module based on channel
affinity assists the feature map to avoid possible redundancy by emphasizing
its distinct channels. The performance on both synthetic and real-world point
clouds datasets demonstrate the superiority and applicability of our network.
Comparing with other state-of-the-art methods, our approach balances accuracy
and efficiency.

Computer Vision and Pattern Recognition;Machine Learning;Robotics;Image and Video Processing