CasA: A Cascade Attention Network for 3-D Object Detection From LiDAR Point Clouds

Three-dimensional object detection from light detection and ranging (LiDAR) point clouds has gained great attention in recent years due to its wide applications in smart cities and autonomous driving. Cascade framework shows its advancement in 2-D object detection but is less investigated in 3-D space. Conventional cascade structures use multiple separate subnetworks to sequentially refine region proposals. Such methods, however, have limited ability to measure proposal quality in all the stages, and it is hard to achieve a desirable performance improvement in 3-D space. This article proposes a new cascade framework, termed Cascade Attention (CasA), for 3-D object detection from LiDAR point clouds. CasA consists of a region proposal network (RPN) and a cascade refinement network (CRN). In CRN, we designed a new cascade attention module (CAM) that uses multiple subnetworks and attention modules to aggregate the object features from different stages and progressively refine region proposals. CasA can be integrated into various two-stage 3-D detectors and improve their performance. Extensive experiments on the KITTI and Waymo datasets with various baseline detectors demonstrate the universality and superiority of our CasA. In particular, based on one variant of Voxel-region-based convolutional neural network (RCNN), we achieve the state-of-the-art results on the KITTI dataset. On the KITTI online 3-D object detection leaderboard, we achieve a high detection performance of 83.06%, 47.09%, and 73.47% average precision (AP) in the moderate car, pedestrian, and cyclist classes, respectively. Code is available at https://github.com/hailanyi/CasA