| --- |
| license: mit |
| tags: |
| - collaborative-perception |
| - v2x |
| - 3d-object-detection |
| - autonomous-driving |
| - lidar |
| --- |
| |
| # FocalComm: Hard Instance-Aware Multi-Agent Perception (WACV 2026) |
|
|
| Pretrained checkpoints for [FocalComm](https://github.com/scdrand23/FocalComm), a collaborative perception framework that exchanges hard-instance-oriented features among connected agents via progressive Hard Instance Mining (HIM) and Query-guided Adaptive Feature Fusion (QAFF). |
|
|
| ## Checkpoints |
|
|
| | File | Dataset | Config | AP@0.3/AP@0.5 | |
| |------|---------|--------|---------------| |
| | `focalcomm_v2xreal/net_epoch50.pth` | V2X-Real (vehicle-centric) | FocalComm 3-stage HIM, epoch 50 | Car 91.6/88.4, Pedestrian 53.2/26.1, Truck 50.4/47.2, Overall 65.1/53.9 | |
| | `focalcomm_dairv2x/net_epoch50.pth` | DAIR-V2X | FocalComm, epoch 50 | Vehicle 73.3/66.4 | |
|
|
| Each checkpoint directory includes the exact training `config.yaml`. The DAIR-V2X checkpoint reproduces the paper result exactly; the V2X-Real checkpoint is the final epoch of the paper's training run (within ~2 mAP of the paper's Table 1 row). |
|
|
| ## Usage |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download scdrand23/FocalComm --local-dir checkpoints |
| |
| python focalcomm/tools/inference.py --model_dir checkpoints/focalcomm_v2xreal --fusion_method intermediate |
| ``` |
|
|
| See the [GitHub repository](https://github.com/scdrand23/FocalComm) for installation and dataset preparation. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
