mlsanityData to 7 Oct 2026

LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection

arXiv 2402.11735 ↗First posted 18 Feb 2024cs.RO cs.CVcited by 10

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Abstract (arXiv)

We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

32 of 49 references.

References · 32

  1. 2020End-to-End Object Detection with Transformerscited by 7,384
  2. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  6. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
  7. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  8. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 655
  9. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  10. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
  11. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  12. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
20 more
  1. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
  2. 2019MVX-Net: Multimodal VoxelNet for 3D Object Detectioncited by 162
  3. 2020CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detectioncited by 159
  4. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  5. 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
  6. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  7. 2020RADIATE: A Radar Dataset for Automotive Perception in Bad Weathercited by 98
  8. 2020Adaptive Context-Aware Multi-Modal Network for Depth Completioncited by 69
  9. 2021Multi-Modal 3D Object Detection in Autonomous Driving: a Surveycited by 63
  10. 2021RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localizationcited by 49
  11. 2020RadarNet: Exploiting Radar for Robust Perception of Dynamic Objectscited by 48
  12. 2022MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environmentscited by 40
  13. 2022Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radarcited by 33
  14. 2022Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Cloudscited by 29
  15. 2022aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perceptioncited by 29
  16. 2023Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Datasetcited by 28
  17. 2018Robust Deep Multi-modal Learning Based on Gated Information Fusion Networkcited by 18
  18. 2022DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radarscited by 16
  19. 2021Radar Voxel Fusion for 3D Object Detectioncited by 13
  20. 2022Convolutional Bayesian Kernel Inference for 3D Semantic Mappingcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 4.

10 papers.