| --- |
| 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} |
| } |
| ``` |
|
|
