PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

TLDR

The 3D voxel CNN’s efficient learning and high‑quality proposals, combined with the flexible receptive fields of PointNet‑based networks, provide a strong foundation for accurate object detection. PV‑RCNN is introduced as a novel, high‑performance framework for accurate 3D object detection from point clouds. PV‑RCNN fuses a 3D voxel CNN with PointNet‑based set abstraction, first summarizing the scene into keypoints via a voxel set abstraction module, then using RoI‑grid pooling to extract proposal‑specific features from these keypoints, thereby learning highly discriminative point‑cloud representations. Experiments on the KITTI and Waymo Open datasets demonstrate that PV‑RCNN surpasses state‑of‑the‑art 3D detection methods by a remarkable margin.

Abstract

We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud features. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks. Specifically, the proposed framework summarizes the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel voxel set abstraction module to save follow-up computations and also to encode representative scene features. Given the high-quality 3D proposals generated by the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific features from the keypoints to the RoI-grid points via keypoint set abstraction. Compared with conventional pooling operations, the RoI-grid feature points encode much richer context information for accurately estimating object confidences and locations. Extensive experiments on both the KITTI dataset and the Waymo Open dataset show that our proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins.