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Cited by 155Autonomous Vehicle Technology and SafetyAdvanced Neural Network ApplicationsAdvanced Vision and Imaging

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 access
Kim, Youngseok, Shin, Juyeb, Kim, Sanmin, Lee, In-Jae, Choi, Jun Won, Kum, Dongsuk·2023·2023 IEEE/CVF International Conference on Computer Vision (ICCV)
DOI 10.1109/iccv51070.2023.01615
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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.

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Crossref · 10.1109/iccv51070.2023.01615

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63 of 63 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 62 references

Research works printed but not linked: 16

  1. Chaabane, Deft: Detection embeddings for tracking
  2. Continental ARS 408-21 Datasheet
  3. Fischer, Cc-3dt: Panoramic 3d object tracking via cross-camera fusion, 2294
  4. Johnson, 1992, Array signal processing: concepts and techniques.
  5. Kim, Low-level sensor fusion network for 3d vehicle detection using radar range-azimuth heatmap and monocular image, 388
  6. Li, Unifying voxel-based representation with transformer for 3d object detection
  7. 10.1007/978-3-031-20077-9_1 · DOI · Paper page
  8. Liang, Bevfusion: A simple and robust lidar-camera fusion framework
  9. Loshchilov, Decoupled weight decay regularization
  10. Meyer, Automotive radar dataset for deep learning based 3d object detection, 129
  11. Park, Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection
  12. Qi, Pointnet: Deep learning on point sets for 3d classification and segmentation, 652
  13. Qi, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, 5105
  14. Roh, Sparse detr: Efficient end-to-end object detection with learnable sparsity
  15. Vaswani, Attention is all you need, 6000
  16. Zhu, Deformable detr: Deformable transformers for end-to-end object detection

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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)
ORCID
Venue
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2023-10-01
Pages
17569-17580
DOI
10.1109/iccv51070.2023.01615
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
Licence
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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