EB-LG Module for 3D Point Cloud Classification and Segmentation
Jintao Chen, Yan Zhang, Feifan Ma, Zhuangbin Tan
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.