Multiscale Region-Based Convolutional Neural Networks for 3D Object Detection with LiDAR Sensors

LiDAR-based 3D object detection plays an essential role for autonomous driving vehicles under poor lighting condition environments. With LiDAR data, the significance of point cloud technologies is increasing important since Li-DAR sensors are largely cost down. However, the sparsity of point cloud poses a challenge for 3D object detection such that the advancements of sparse convolutional networks could be further investigated. Considering multiscale feature fusion mechanism can improve the object detection performances by using the rich inform among various scale features, we properly add the refinement fusion network with cross-attention modules to the existed 3D voxel-based object detection networks. Additionally, we a realistic strategy to refine the existing point-cloud data augmentation techniques to enable the trained detection networks to achieve substantially improved results. The experimental results demonstrate the effectiveness of our proposed detection system across three categories on the KITTI dataset. These enhancements address the limitations of current approaches and highlight the superior performance of the proposed system.