mlsanityData to 7 Oct 2026

CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion

arXiv 2310.06008 ↗First posted 9 Oct 2023cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. By sharing sensor data between Connected Autonomous Vehicles (CAVs), the safety and reliability of these vehicles can be improved through a concept known as cooperative perception. However, recent approaches in cooperative perception only share single sensor information such as cameras or LiDAR. In this research, we explore the fusion of multiple sensor data sources and present a framework, called CoBEVFusion, that fuses LiDAR and camera data to create a Bird's-Eye View (BEV) representation. The CAVs process the multi-modal data locally and utilize a Dual Window-based Cross-Attention (DWCA) module to fuse the LiDAR and camera features into a unified BEV representation. The fused BEV feature maps are shared among the CAVs, and a 3D Convolutional Neural Network is applied to aggregate the features from the CAVs. Our CoBEVFusion framework was evaluated on the cooperative perception dataset OPV2V for two perception tasks: BEV semantic segmentation and 3D object detection. The results show that our DWCA LiDAR-camera fusion model outperforms perception models with single-modal data and state-of-the-art BEV fusion models. Our overall cooperative perception architecture, CoBEVFusion, also achieves comparable performance with other cooperative perception models.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 36 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

32 of 36 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
  9. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
  10. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  11. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  12. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
20 more
  1. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  2. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  3. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  4. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  5. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  6. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  7. 2022DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 270
  8. 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
  9. 2022CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformerscited by 227
  10. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  11. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  12. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  13. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  14. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  15. 2019Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscited by 110
  16. 2021FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detectioncited by 92
  17. 2022Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Cloudscited by 29
  18. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  19. 2020Cooperative LIDAR Object Detection via Feature Sharing in Deep Networkscited by 19
  20. 2019TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Datacited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024TUMTraf V2X Cooperative Perception Datasetcited by 66
  2. 2025An Initial Study of Bird's-Eye View Generation for Autonomous Vehicles using Cross-View Transformerscited by 2
  3. 2025Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Surveycited by 1
  4. 2026Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Predictioncited by 1
  5. 2026SiMO: Single-Modality-Operable Multimodal Collaborative Perceptioncited by 0
  6. 2026CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentationcited by 0