mlsanityData to 14 Oct 2026

CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking

arXiv 2403.15313 ↗First posted 22 Mar 2024cs.CV cs.AIcited by 10

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Abstract (arXiv)

To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive systems, their potential in 3D detection and tracking has been largely disregarded due to data sparsity and measurement noise. As a recent development, the combination of RADARs and cameras is emerging as a promising solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities, by incorporating the spatial and velocity information of the RADAR sensor. Experimental results demonstrate an absolute improvement in detection performance of 5.3% in mean Average Precision (mAP) and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between high-performance and cost-effective perception systems in autonomous driving, by capitalizing on the ubiquitous presence of RADAR in automotive applications. The code is available at: https://github.com/ETH-PBL/CR3DT.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 30 of its 35 references that are in the map. First posted on arXiv 22 Mar 2024.

Builds on

30 of 35 references.

References · 30

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  6. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  7. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
  8. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  9. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 713
  10. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  11. 2020Tracking Objects as Pointscited by 483
  12. 2022BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detectioncited by 341
18 more
  1. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  2. 2023Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detectioncited by 315
  3. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  4. 2022Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?cited by 128
  5. 2021Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Surveycited by 82
  6. 2023CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perceptioncited by 82
  7. 2021Monocular Quasi-Dense 3D Object Trackingcited by 81
  8. 2020A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detectioncited by 70
  9. 2022QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Trackingcited by 61
  10. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 58
  11. 2023LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusioncited by 58
  12. 2023FocalFormer3D : Focusing on Hard Instance for 3D Object Detectioncited by 58
  13. 2021DEFT: Detection Embeddings for Trackingcited by 56
  14. 2021Exploring Simple 3D Multi-Object Tracking for Autonomous Drivingcited by 56
  15. 2023Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Predictioncited by 49
  16. 2022HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detectioncited by 31
  17. 2022CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusioncited by 31
  18. 2023LinK: Linear Kernel for LiDAR-based 3D Perceptioncited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 5.

10 papers.

  1. 2024MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Drivingcited by 17
  2. 2025SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weathercited by 12
  3. 2025Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Networkcited by 2
  4. 2025OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filteringcited by 1
  5. 2026Radar-Informed 3D Multi-Object Tracking under Adverse Conditionscited by 1
  6. 2024A Generative Adversarial Network-based Method for LiDAR-Assisted Radar Image Enhancementcited by 0
  7. 2026Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusioncited by 0
  8. 20264D Radar Perception Algorithms for Autonomous Driving: A Reviewcited by 0
  9. 2026SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detectioncited by 0
  10. 2026Adversarial Robustness of Camera-Radar Sensor Fusion for Autonomous Drivingcited by 0