---
license: mit
tags:
- collaborative-perception
- v2x
- 3d-object-detection
- autonomous-driving
- vector-quantization
- compression
---

# ReVQom: Residual Vector Quantization for Communication-Efficient Multi-Agent Perception (ICASSP 2026)

Pretrained checkpoints for [ReVQom](https://github.com/scdrand23/ReVQom), a learned feature codec for multi-agent collaborative perception. ReVQom compresses BEV features via a 1x1 bottleneck and multi-stage residual vector quantization, transmitting only per-pixel code indices (6-30 bits per pixel, 273x-1365x compression vs raw features).

## Checkpoints

| File | Config | Dataset | AP@0.3/AP@0.5 |
|------|--------|---------|---------------|
| `revqom_s_k64_dairv2x/net_epoch30.pth` | ReVQom-S: K=64, n_q=3, C_rr=16, EMA 0.8 (18 bpp, 455x) | DAIR-V2X | 0.751/0.646 |

The checkpoint directory includes the exact training `config.yaml`, and the evaluation matches the paper's experiment records. Additional checkpoints (ReVQom-M, K=256) will be added after re-evaluation.

## Usage

```bash
pip install -U huggingface_hub
hf download scdrand23/ReVQom --local-dir checkpoints

python revqom/tools/inference.py --model_dir checkpoints/revqom_s_k64_dairv2x --fusion_method intermediate
```

See the [GitHub repository](https://github.com/scdrand23/ReVQom) for installation and dataset preparation.

## Citation

```bibtex
@inproceedings{shenkut2026revqom,
  title={Residual Vector Quantization for Communication-Efficient Multi-Agent Perception},
  author={Shenkut, Dereje and Kumar, B.V.K. Vijaya},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2026}
}
```