CWGA-Net: Center-Weighted Graph Attention Network for 3D object detection from point clouds

Abstract

The precision of 3D object detection from unevenly distributed outdoor point clouds is critical in autonomous driving perception systems. Current point-based detectors employ self-attention and graph convolution to establish contextual relationships between point clouds; however, they often introduce weakly correlated redundant information, leading to blurred geometric details and false detections. To address this issue, a novel Center-weighted Graph Attention Network (CWGA-Net) has been proposed to fuse geometric and semantic similarities for weighting cross-attention scores, thereby capturing precise fine-grained geometric features. CWGA-Net initially constructs and encodes local graphs between foreground points, establishing connections between point clouds from geometric and semantic dimensions. Subsequently, center-weighted cross-attention is utilized to compute the contextual relationships between vertices within the graph, and geometric and semantic similarities between vertices are fused to weight attention scores, thereby extracting strongly related geometric shape features. Finally, a cross-feature fusion Module is introduced to deeply fuse high and low-resolution features to compensate for the information loss during downsampling. Experiments conducted on the KITTI and Waymo datasets demonstrate that the network achieves superior detection capabilities, outperforming state-of-the-art point-based single-stage methods in terms of average precision metrics while maintaining good speed.