The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Open accessAbstract
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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“CRN [101] uses Radar-Assisted View Transformation (RVT) to align camera and radar features in BEV and applies multimodal deformable cross-attention (MDCA) for precise fusion.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [101]
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Metadata
- Authors
- Kim, Y., Shin, J., Kim, S., Lee, I.-J., Choi, J. W., & Kum, D.
- Affiliations
- KAIST
- Korea Advanced Institute of Science and Technology (KR)
- Hanyang University (KR)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 17569-17580
- OpenAlex
- W4390872227
- Cited by
- 155
- References
- 62
- Citations by year
- 2026: 45 · 2025: 64 · 2024: 37 · 2023: 9
- FWCI
- 48.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation of Korea
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- radar-camera fusion, 3D object detection, autonomous driving, low-cost radar, sparse point clouds, semantic segmentation, object tracking, real-time perception, nuScenes
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