{"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/lidar-r-cnn-an-efficient-and-universal-3d","title":"LiDAR R-CNN: An Efficient and Universal 3D Object Detector","arxiv_id":"2103.15297","date":"2021-03-29","proceeding":"CVPR 2021 1","authors":["Zhichao Li","Feng Wang","Naiyan Wang"],"abstract":"LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that can generally improve any existing 3D detector. To fulfill the real-time and high precision requirement in practice, we resort to point-based approach other than the popular voxel-based approach. However, we find an overlooked issue in previous work: Naively applying point-based methods like PointNet could make the learned features ignore the size of proposals. To this end, we analyze this problem in detail and propose several methods to remedy it, which bring significant performance improvement. Comprehensive experimental results on real-world datasets like Waymo Open Dataset (WOD) and KITTI dataset with various popular detectors demonstrate the universality and superiority of our LiDAR R-CNN. In particular, based on one variant of PointPillars, our method could achieve new state-of-the-art results with minor cost. Codes will be released at https://github.com/tusimple/LiDAR_RCNN .","url_abs":"https://arxiv.org/abs/2103.15297v1","url_pdf":"https://arxiv.org/pdf/2103.15297v1.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":"lidar-r-cnn-an-efficient-and-universal-3d","repo_url":"https://github.com/tusimple/LiDAR_RCNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.15297","atlas_url":"https://app.syntology.ai/?focus=2103.15297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15297"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/tusimple/LiDAR_RCNN","reach":null}],"summary":{"ran":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"6d77160eac6886d0","entry":"PointNet","repo":"tusimple/LiDAR_RCNN","repo_kind":"official","path":"src/LiDAR_RCNN/models/point_net.py","file_url":"https://github.com/tusimple/LiDAR_RCNN/blob/HEAD/src/LiDAR_RCNN/models/point_net.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6d77160eac6886d0"}},{"code_sha256_prefix":"5012856ae6c4719d","entry":"PointNetfeat","repo":"tusimple/LiDAR_RCNN","repo_kind":"official","path":"src/LiDAR_RCNN/models/point_net.py","file_url":"https://github.com/tusimple/LiDAR_RCNN/blob/HEAD/src/LiDAR_RCNN/models/point_net.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5012856ae6c4719d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}