metadata
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, 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
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 for installation and dataset preparation.
Citation
@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}
}