{"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/crn-camera-radar-net-for-accurate-robust","title":"CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception","arxiv_id":"2304.00670","date":"2023-04-03","proceeding":"ICCV 2023 1","authors":["Youngseok Kim","Juyeb Shin","Sanmin Kim","In-Jae Lee","Jun Won Choi","Dongsuk Kum"],"abstract":"Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approaches have shown promising results, they are susceptible to poor illumination or bad weather conditions and have a large localization error. Hence, fusing camera with low-cost radar, which provides precise long-range measurement and operates reliably in all environments, is promising but has not yet been thoroughly investigated. In this paper, we propose Camera Radar Net (CRN), a novel camera-radar fusion framework that generates a semantically rich and spatially accurate bird's-eye-view (BEV) feature map for various tasks. To overcome the lack of spatial information in an image, we transform perspective view image features to BEV with the help of sparse but accurate radar points. We further aggregate image and radar feature maps in BEV using multi-modal deformable attention designed to tackle the spatial misalignment between inputs. CRN with real-time setting operates at 20 FPS while achieving comparable performance to LiDAR detectors on nuScenes, and even outperforms at a far distance on 100m setting. Moreover, CRN with offline setting yields 62.4% NDS, 57.5% mAP on nuScenes test set and ranks first among all camera and camera-radar 3D object detectors.","url_abs":"https://arxiv.org/abs/2304.00670v3","url_pdf":"https://arxiv.org/pdf/2304.00670v3.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":"crn-camera-radar-net-for-accurate-robust","repo_url":"https://github.com/youngskkim/CRN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-multi-object-tracking","task_name":"3D Multi-Object Tracking"},{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"3d-object-tracking","task_name":"3D Object Tracking"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"crn","method_name":"CRN"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-multi-object-tracking-on-nuscenes","task":"3D Multi-Object Tracking","dataset":"nuScenes","model":"CRN","rank_in_archive_order":62,"of":115,"metrics":{"AMOTA":"0.569"},"uses_additional_data":false},{"leaderboard":"/sota/3d-multi-object-tracking-on-nuscenes-camera-2","task":"3D Multi-Object Tracking","dataset":"nuscenes Camera-Radar","model":"CRN","rank_in_archive_order":3,"of":4,"metrics":{"AMOTA":"0.569"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-nuscenes","task":"3D Object Detection","dataset":"nuScenes","model":"CRN","rank_in_archive_order":154,"of":372,"metrics":{"NDS":"0.624","mAP":"0.575"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-nuscenes-camera-radar","task":"3D Object Detection","dataset":"nuscenes Camera-Radar","model":"CRN","rank_in_archive_order":5,"of":11,"metrics":{"NDS":"62.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2304.00670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.00670"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. 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