CoChat
Sign Up
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/iccv51070.2023.01615

Cites

63 of 63 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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

Cited by

Sorted by citations

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review