3D Object Detection Method Based on CA Sampling and Local Attention Feature Encoding.

The utilization of LiDAR point cloud in various applications, such as autonomous driving and intelligent transportation, has gained significant attention and become a research hotspot. However, existing 3D object detection methods face two main challenges. (1) random or farthest point sampling can lead to a loss of foreground points and missed detections, particularly for small objects. (2) the sparsity of LiDAR point clouds causes the object point clouds to be non-uniformly distributed. In this paper, we present a novel method for 3D object detection. Our approach is based on the PV-RCNN network architecture and incorporates Centroid-aware (CA) Sampling and Local Attention Feature Encoding techniques. The voxel branch in feature extraction uses the Focals Conv module to predict the importance of voxel features at different locations and enhances the representation of local spatial structure by attention convolution. The key point branch uses CA Sampling to obtain key points. We calculate mask weights based on the position relationship between each point and the 3D bounding boxes, and the K highest scoring points are retained as key points. We further improve the local feature expression capability by aggregating each key point with the original point cloud and voxel features in the neighborhood using the PointNet++ network. Finally, we combine the collocated features and feed them into the classification and regression network of PV-RCNN to complete the detection process. Our simulation experiments show that the detection performance of our proposed method outperforms the PV-RCNN on the KITTI dataset.