mlsanityData to 7 Oct 2026

Full-Velocity Radar Returns by Radar-Camera Fusion

arXiv 2108.10637 ↗First posted 24 Aug 2021cs.CVcited by 16

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Abstract (arXiv)

A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

25 of 38 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  5. 2018Object Detection with Deep Learning: A Reviewcited by 524
  6. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 251
  7. 2020CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detectioncited by 159
  8. 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
  9. 2020Kinematic 3D Object Detection in Monocular Videocited by 116
  10. 2020Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Reviewcited by 114
  11. 2020Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systemscited by 100
  12. 2020A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detectioncited by 70
13 more
  1. 2021Radar-Camera Pixel Depth Association for Depth Completioncited by 49
  2. 2020RadarNet: Exploiting Radar for Robust Perception of Dynamic Objectscited by 48
  3. 2020CNN based Road User Detection using the 3D Radar Cubecited by 47
  4. 2019Distant Vehicle Detection Using Radar and Visioncited by 44
  5. 2019Depth Coefficients for Depth Completioncited by 41
  6. 2021Depth Completion with Twin Surface Extrapolation at Occlusion Boundariescited by 41
  7. 20192D Car Detection in Radar Data with PointNetscited by 38
  8. 2019Deep Radar Detectorcited by 22
  9. 2020RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radarcited by 22
  10. 2018ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentationcited by 15
  11. 2019A Multi-Stage Clustering Framework for Automotive Radar Datacited by 6
  12. 2019BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloudcited by 3
  13. 2018A Pyramid CNN for Dense-Leaves Segmentationcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 6; 2024: 4; 2025: 5.

16 papers.

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  1. 2022CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformercited by 66
  2. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  3. 2023MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusioncited by 29
  4. 2023Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Reviewcited by 22
  5. 2024RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scalecited by 20
  6. 2024RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptionscited by 20
  7. 2023Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completioncited by 12
  8. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  9. 2024Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Surveycited by 9
  10. 2025RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detectioncited by 9
  11. 2024RIDERS: Radar-Infrared Depth Estimation for Robust Sensingcited by 5
  12. 2025Radar-Guided Polynomial Fitting for Metric Depth Estimationcited by 5
4 more
  1. 2025Structure-Aware Radar-Camera Depth Estimationcited by 5
  2. 2023Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detectioncited by 4
  3. 2025DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidancecited by 4
  4. 2025RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimationcited by 2