{"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/frustum-pointnets-for-3d-object-detection","title":"Frustum PointNets for 3D Object Detection from RGB-D Data","arxiv_id":"1711.08488","date":"2017-11-22","proceeding":"CVPR 2018 6","authors":["Charles R. Qi","Wei Liu","Chenxia Wu","Hao Su","Leonidas J. Guibas"],"abstract":"In this work, we study 3D object detection from RGB-D data in both indoor and\noutdoor scenes. While previous methods focus on images or 3D voxels, often\nobscuring natural 3D patterns and invariances of 3D data, we directly operate\non raw point clouds by popping up RGB-D scans. However, a key challenge of this\napproach is how to efficiently localize objects in point clouds of large-scale\nscenes (region proposal). Instead of solely relying on 3D proposals, our method\nleverages both mature 2D object detectors and advanced 3D deep learning for\nobject localization, achieving efficiency as well as high recall for even small\nobjects. Benefited from learning directly in raw point clouds, our method is\nalso able to precisely estimate 3D bounding boxes even under strong occlusion\nor with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection\nbenchmarks, our method outperforms the state of the art by remarkable margins\nwhile having real-time capability.","url_abs":"http://arxiv.org/abs/1711.08488v2","url_pdf":"http://arxiv.org/pdf/1711.08488v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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