{"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","title":"PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection","arxiv_id":"1912.13192","date":"2019-12-31","proceeding":"CVPR 2020 6","authors":["Shaoshuai Shi","Chaoxu Guo","Li Jiang","Zhe Wang","Jianping Shi","Xiaogang Wang","Hongsheng Li"],"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. 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