Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
This work improves 3D object detection for autonomous driving by introducing a deformable refinement module, though it is incremental as it builds on existing two-stage detectors.
The paper tackled the problem of 3D object detection in point clouds by addressing limitations in proposal refinement for varying scales, density, and deformations, resulting in state-of-the-art performance on the KITTI dataset.
We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector. Currently, the proposal refinement methods used by the state-of-the-art two-stage detectors cannot adequately accommodate differing object scales, varying point-cloud density, part-deformation and clutter. We present a proposal refinement module inspired by 2D deformable convolution networks that can adaptively gather instance-specific features from locations where informative content exists. We also propose a simple context gating mechanism which allows the keypoints to select relevant context information for the refinement stage. We show state-of-the-art results on the KITTI dataset.