CSANet: Cuboid-Wise Shape Augmentation 3D Object Detector for Occluded Targets

For 3D environmental perception tasks, light detection and ranging (LiDAR) assumes a crucial role by supplying extensive data in 3D space. In the context of deep learning-based LiDAR-only 3D object detectors, practical challenges like occlusion and signal missing lead to the loss of partial shape information, thereby causing a decline in detection accuracy. To tackle this issue, we propose a two-stage LiDAR 3D object detector that includes the cuboid-wise shape augmentation network to supplement instance-level foreground points with geometric structure information. Dense point clouds are achieved by introducing gridding procedure and 3D encoder-decoder. Besides, we further capture contextual information and reweight grid points within each proposal. The original region of interest (RoI) features are aggregated with the augmented features in refinement stage to recover intact shape details. The performance of our proposed method on KITTI dataset demonstrates its effectiveness in 3D object detection.