Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

• URL: http://arxiv.org/abs/2008.08766v1
• Date: Thu, 20 Aug 2020 04:11:17 GMT
• Title: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
• Authors: Prarthana Bhattacharyya and Krzysztof Czarnecki
• Abstract summary: We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector. We present a proposal refinement module inspired by 2D deformable convolution networks. We show state-of-the-art results on the KITTI dataset.
• Score: 11.462554246732683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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