3D Object Detection Combining Semantic and Geometric Features from Point Clouds

In this paper, we investigate the combination of voxel-based methods and point-based methods, and propose a novel end-to-end two-stage 3D object detector named SGNet for point clouds scenes. The voxel-based methods voxelize the scene to regular grids, which can be processed with the current advanced feature learning frameworks based on convolutional layers for semantic feature learning. Whereas the point-based methods can better extract the geometric feature of the point due to the coordinate reservations. The combination of the two is an effective solution for 3D object detection from point clouds. However, most current methods use a voxel-based detection head with anchors for final classification and localization. Although the preset anchors cover the entire scene, it is not suitable for point clouds detection tasks with larger scenes and multiple categories due to the limitation of voxel size. In this paper, we propose a voxel-to-point module (VTPM) that captures semantic and geometric features. The VTPM is a Voxel-Point-Based Module that finally implements 3D object detection in point space, which is more conducive to the detection of small-size objects and avoids the presets of anchors in inference stage. In addition, a Confidence Adjustment Module (CAM) with the center-boundary-aware confidence attention is proposed to solve the misalignment between the predicted confidence and proposals in the regions of the interest (RoI) selection. The SGNet proposed in this paper has achieved state-of-the-art results for 3D object detection in the KITTI dataset, especially in the detection of small-size objects such as cyclists. Actually, as of September 19, 2021, for KITTI dataset, SGNet ranked 1st in 3D and BEV detection on cyclists with easy difficulty level, and 2nd in the 3D detection of moderate cyclists.