Papers › CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Youngseok Kim, Juyeb Shin, Sanmin Kim, In-Jae Lee, Jun Won Choi, Dongsuk Kum
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Multi-Object Tracking | nuScenes | CRN | AMOTA | 0.569 | #62 of 115 | Archive leaderboard | report |
| 3D Multi-Object Tracking | nuscenes Camera-Radar | CRN | AMOTA | 0.569 | #3 of 4 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | CRN | NDS | 0.624 | #154 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | CRN | mAP | 0.575 | #154 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuscenes Camera-Radar | CRN | NDS | 62.4 | #5 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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