EB-LG Module for 3D Point Cloud Classification and Segmentation

Abstract

Thanks to the development of deep learning technology and computer science, 3D point cloud analysis is becoming a research hotspot. Based on convolution operator, local feature learning is fundamental but far from perfect regarding point cloud analysis task. Actually, when people see a wing-like part (local features), our brain immediately associates it with a plane model (global features), and then we further trust that it's a wing based on the feedback from our brain. Such hidden features between local features and global features are commonly-existed, but ignored by existing methods. To this end, we propose Error feature Back-projection based Local-Global (EB-LG) feature learning module for better representations of point clouds. Specifically, EB-LG module adequately captures the hidden features firstly; and then borrowing from the successful idea of error-feedback mechanism, the learned hidden features will be back-projected to original local features, so that the enhanced local features are obtained. Serving as a plug-and-play, EB-LG module is lightweight and can be easily integrated into existing state-of-the-art networks to boost their performance. Extensive evaluations on both synthetic and real-world 3D point cloud benchmarks demonstrate the effectiveness and the generalization ability of our method.