Where2comm

Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen
Presented at Neurips 2022 Spotlight

Single agent detection v.s. collaborative perception

See our code at https://github.com/MediaBrain-SJTU/Where2comm.

Main idea

Abstract: Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate.

Features

• Dataset Support

  • DAIR-V2X
  • OPV2V
  • V2X-Sim 2.0

• SOTA collaborative perception method support

  • Where2comm [Neurips2022]
  • V2VNet [ECCV2020]
  • DiscoNet [NeurIPS2021]
  • V2X-ViT [ECCV2022]
  • When2com [CVPR2020]
  • Late Fusion
  • Early Fusion

• Visualization

  • BEV visualization
  • 3D visualization

Citation

If you find this code useful in your research then please cite

@inproceedings{Where2comm:22,
  author    = {Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen},
  title     = {Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps},
  booktitle = {Thirty-sixth Conference on Neural Information Processing Systems (Neurips)},
  month     = {November},  
  year      = {2022}
}

Acknowledgements

Thank for the excellent cooperative perception codebases OpenCOOD and CoPerception.

Thank for the excellent cooperative perception datasets DAIR-V2X, OPV2V and V2X-SIM.

Thank for the dataset and code support by YiFan Lu.

Relevant Projects

Thanks for the insightful previous works in cooperative perception field.

V2vnet: Vehicle-to-vehicle communication for joint perception and prediction ECCV20 [Paper]

When2com: Multi-agent perception via communication graph grouping CVPR20 [Paper] [Code]

Who2com: Collaborative Perception via Learnable Handshake Communication ICRA20 [Paper]

Learning Distilled Collaboration Graph for Multi-Agent Perception Neurips21 [Paper] [Code]

V2X-Sim: A Virtual Collaborative Perception Dataset and Benchmark for Autonomous Driving RAL21 [Paper] [Website][Code]

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication ICRA2022 [Paper] [Website] [Code]

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer ECCV2022 [Paper] [Code] [Talk]

Self-Supervised Collaborative Scene Completion: Towards Task-Agnostic Multi-Robot Perception CoRL2022 [Paper]

CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers CoRL2022 [Paper] [Code]

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection CVPR2022 [Paper] [Website] [Code]

Contact

If you have any problem with this code, please feel free to contact 18671129361@sjtu.edu.cn.