SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features

With the increasing demand from unmanned driving and robotics, more attention has been paid to point-cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features regarding reflectivity of the point cloud to maximize the use of information in the point cloud. Secondly, we enhanced the distance features and raw features, which we collectively refer to as self-features of the key points, in set abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show that the enhancement method proposed in this paper has excellent performance.