AV PV-RCNN: Improving 3D Object Detection with Adaptive Deformation and VectorPool Aggregation

Three-dimensional object detection has attracted more and more attention from industry and academia due to its wide application in various fields such as autonomous driving and robotics. Currently, the refinement methods used by advanced two-stage detectors cannot fully adapt to different object scales, different point cloud densities, partial deformation and clutter, and excessive resource consumption. We propose a point cloud-based 3D object detection method that can adapt to different object scales and aggregate local features with less resources. The method first passes through an adaptive deformation module based on a 2D deformable convolutional network, which can adaptively collect instance-specific features from where the information content exists. Secondly, through a VectorPool aggregation module, this module can better aggregate local point features with less resource consumption. Finally, through a context fusion module, the key points can filter out relevant context information for the refinement stage. Our proposed detection method not only achieves better accuracy on the KITTI dataset, but also consumes less resources than the original detectors and has faster inference speed.