{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pv-rcnn-point-voxel-feature-set-abstraction-1","title":"PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection","arxiv_id":"2102.00463","date":"2021-01-31","proceeding":null,"authors":["Shaoshuai Shi","Li Jiang","Jiajun Deng","Zhe Wang","Chaoxu Guo","Jianping Shi","Xiaogang Wang","Hongsheng Li"],"abstract":"3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields. In this paper, we propose Point-Voxel Region-based Convolution Neural Networks (PV-RCNNs) for 3D object detection on point clouds. First, we propose a novel 3D detector, PV-RCNN, which boosts the 3D detection performance by deeply integrating the feature learning of both point-based set abstraction and voxel-based sparse convolution through two novel steps, i.e., the voxel-to-keypoint scene encoding and the keypoint-to-grid RoI feature abstraction. Second, we propose an advanced framework, PV-RCNN++, for more efficient and accurate 3D object detection. It consists of two major improvements: sectorized proposal-centric sampling for efficiently producing more representative keypoints, and VectorPool aggregation for better aggregating local point features with much less resource consumption. With these two strategies, our PV-RCNN++ is about $3\\times$ faster than PV-RCNN, while also achieving better performance. The experiments demonstrate that our proposed PV-RCNN++ framework achieves state-of-the-art 3D detection performance on the large-scale and highly-competitive Waymo Open Dataset with 10 FPS inference speed on the detection range of 150m * 150m.","url_abs":"https://arxiv.org/abs/2102.00463v3","url_pdf":"https://arxiv.org/pdf/2102.00463v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pv-rcnn-point-voxel-feature-set-abstraction-1","repo_url":"https://github.com/open-mmlab/OpenPCDet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-easy","task":"3D Object Detection","dataset":"KITTI Cars Easy","model":"PV-RCNN++","rank_in_archive_order":9,"of":26,"metrics":{"AP":"90.14%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-easy-val","task":"3D Object Detection","dataset":"KITTI Cars Easy val","model":"PV-RCNN++","rank_in_archive_order":2,"of":11,"metrics":{"AP":"92.57"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-hard","task":"3D Object Detection","dataset":"KITTI Cars Hard","model":"PV-RCNN++","rank_in_archive_order":4,"of":25,"metrics":{"AP":"77.15%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-hard-val","task":"3D Object Detection","dataset":"KITTI Cars Hard val","model":"PV-RCNN++","rank_in_archive_order":2,"of":10,"metrics":{"AP":"82.69"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-moderate-1","task":"3D Object Detection","dataset":"KITTI Cars Moderate val","model":"PV-RCNN++","rank_in_archive_order":3,"of":11,"metrics":{"AP":"84.83"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-waymo-open-dataset","task":"3D Object Detection","dataset":"Waymo Open Dataset","model":"PV-RCNN++","rank_in_archive_order":6,"of":8,"metrics":{"mAPH/L2":"69.5"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2102.00463","atlas_url":"https://app.syntology.ai/?focus=2102.00463","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}