Official implementation of FocalComm (WACV 2026).

Note: Code release in progress. Full implementation and pretrained models coming soon.

Multi-agent collaborative perception (CP) is a promising paradigm for improving autonomous driving safety, particularly for vulnerable road users like pedestrians, via robust 3D perception. However, existing CP approaches often optimize for vehicle detection performance metrics, underperforming on smaller, safety-critical objects such as pedestrians, where detection failures can be catastrophic. Furthermore, previous CP methods rely on full feature exchange rather than communicating only salient features that help reduce false negatives. To this end, we present FocalComm, a novel collaborative perception framework that focuses on exchanging hard-instance-oriented features among connected collaborative agents. FocalComm consists of two key novel designs: (1) a learnable progressive hard instance mining (HIM) module to extract hard instances-oriented features per agent, and (2) a query-based feature-level (intermediate) fusion technique that dynamically weights these identified features during collaboration.

conda env create -f environment.yaml
conda activate focalcomm

python focalcomm/utils/setup.py build_ext --inplace
cd focalcomm/pcdet_utils && python setup.py build_ext --inplace

python focalcomm/tools/train.py --hypes_yaml focalcomm/hypes_yaml/v2xreal/focalcommv3.yaml

python focalcomm/tools/inference.py --model_dir <checkpoint_path> --hypes_yaml focalcomm/hypes_yaml/v2xreal/focalcommv3.yaml

• V2X-Real
• DAIR-V2X

@inproceedings{shenkut2026focalcomm,
  title={FocalComm: Hard Instance-Aware Multi-Agent Perception},
  author={Shenkut, Dereje and Bhagavatula, Vijayakumar},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2026}
}

MIT License