Dual-Branch Point Cloud Feature Learning for 3D Object Detection

Hengsheng Lun, Jian Xue, Ke Lu

Abstract: Incomplete feature information is a key problem that limits 3D point cloud object detection and its applications. Many state-of-the-art detectors address this problem from different perspectives, but a comprehensive solution has not yet been obtained. In this paper, a solution is proposed that consists of two branches, one for channel-wise local feature learning and one for spatial-wise global feature learning. The combination of the local features, global contextual features, channel-wise attention features, and spatial attention features of the 3D point cloud is obtained through the two branches. Specifically, a generic spatial self-attention model is proposed that uses skeleton convolution to enhance the extraction of spatial features and combines it with a self-attention mechanism to improve the learning of global features. Further, the proposed skeleton attention mechanism focuses on object contour and rotation invariance of the point cloud. Through sufficient experimental validation, all module proposed in this paper are shown to have a substantial effect on performance and the visualization results demonstrate the importance of our approach.