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 uses Radar-Assisted View Transformation to align camera and radar features in BEV and applies multimodal deformable cross-attention for precise fusion.
“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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LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera
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UniBEVFusion: Unified Radar-Vision Bevfusion for 3D Object Detection
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Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
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Advancing Autonomous Driving: DepthSense With Radar and Spatial Attention
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Doppler-Aware LiDAR-RADAR Fusion for Weather-Robust 3D Detection
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SparseInteraction: Sparse Semantic Guidance for Radar and Camera 3D Object Detection
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LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction Using LiDAR and Camera
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CRPlace: Camera-Radar Fusion with BEV Representation for Place Recognition
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Wang, W., Han, J., Jiang, Z., Zhou, Z., & Wu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3525899 · Cited by 6 · see citing works
Multiple Attention Mechanism for Camera-Radar Fusion Object Detection
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RCDFNet: A 4-D Radar and Camera Dual-Level Fusion Network for 3-D Object Detection
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MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV
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Zfusion: an Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving
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Exploring Sensor Impact and Architectural Robustness in Adverse Weather on BEV Perception
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“For the camera and RADAR fusion, we compare the fusion architectures against clean-sensor baselines such as MM-TraP [30] and CRN [85], which achieve vehicle IoU scores up to 58.8% and mIoU scores ranging from 53.8% to 70.5%.”
InfluentialComprehensive Performance Analysis of Object Detection Algorithms Across Diverse Sensor Modalities
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Self-citationProposal-Guided Multi-Scale Radar and Vision Fusion for 3D Object Detection
Li, H., Ling, J., Guo, Z., Huang, X., & Yi, W. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3616963 · Cited by 4 · see citing works
DDCFusion: Dynamic Depth Compensation Fusion for Camera–Radar 3-D Object Detection
Chen, J., Chen, H., Zhu, Z., Shen, Z., Ling, X., & Zhu, Y. M. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3647645 · Cited by 4 · see citing works
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RCAlign: Revisiting Radar Camera Alignment by Contrastive Learning for 3D Object Detection
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RCF-TP: Radar-Camera Fusion With Temporal Priors for 3D Object Detection
Miron, Y., Drews, F., Faion, F., Castro, D. D., & Klein, I. (2024) · IEEE Access · doi:10.1109/access.2024.3408066 · Cited by 4 · see citing works
LRCFormer: lightweight transformer based radar-camera fusion for 3D target detection
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Point Cloud Painting for 3D Object Detection with Camera and Automotive 3+1D RADAR Fusion
Montiel-Marín, S., Llamazares, Á., Antunes, M., Revenga, P. A., & Bergasa, L. M. (2024) · Sensors · doi:10.3390/s24041244 · Cited by 4 · see citing works
RaViDeep: Target Detection Based on Deep Fusion of Radar and Vision in Berthing Scenarios
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Adaptive Cross-Attention Gated Network for Radar-Camera Fusion in BEV Space
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DRCFusion: dual-stream radar-camera fusion for object detection in adverse weather conditions
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RCFusionNet: A Radar-Guided Two-Stage Fusion Approach to Effective Multimodal Perception
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SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving
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RCACF: 4D Radar-Camera Adaptive and Cooperative Fusion for 3D detection
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RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D Object Detection in Autonomous Driving
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DMformer: a transformer with denoising and multi-modal data fusion for enhancing BEV perception
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Improving Radar–Camera Fusion for 3D Object Detection via Eliciting Knowledge From Foundation Model
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L-RadSet: A Long-Range Multimodal Dataset With 4D Radar for Autonomous Driving and Its Application
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Temporal-Enhanced Radar and Camera Fusion for Object Detection
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Radar–Camera Fusion in Perspective View and Bird’s Eye View for 3D Object Detection
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Camera-Based Online Vectorized HD Map Construction With Incomplete Observation
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Semantics-Fusion: Radar Semantic Information-Based Radar–Camera Fusion for 3D Object Detection
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CMGFA: A BEV Segmentation Model Based on Cross-Modal Group-Mix Attention Feature Aggregator
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CaRA-MOT: 3D Multi-Object Tracking with Memory Mechanism using Camera and Radar
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VPA: Multi-Modal Virtual Point Augmentation for 3D Object Detection
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SMAB: Simple Multimodal Attention for Effective BEV Fusion
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REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction
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Self-citationCORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving
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FIN: Fast Inference Network for Map Segmentation
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- 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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