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LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication — arXiv:2610.11264

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.11264
License (as archived): CC0-1.0
Archived: 2026-10-09T06:30:25.611Z
SHA-256 of the archived text: 6cc55443dc78b32decd1c759535165c01c65f60827f9d691d6b37e65c8a9979f
Stable URL of this page: https://metraipolis.com/documento/dc_f697ffcd641f

How to cite

LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication — arXiv:2610.11264 — arXiv (en), https://arxiv.org/abs/2610.11264 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f697ffcd641f (Metraipolis archive dc_f697ffcd641f, sha256 6cc55443dc78b32decd1c759535165c01c65f60827f9d691d6b37e65c8a9979f)

BibTeX

@misc{dcf697ffcd641f,
  title = {LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication — arXiv:2610.11264},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.11264},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f697ffcd641f; sha256 6cc55443dc78b32decd1c759535165c01c65f60827f9d691d6b37e65c8a9979f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.11264. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Given the inherent unpredictability of packet loss in vehicular wireless communications, V2X collaborative perception can yield practical benefits only if agents can achieve reliable collaboration under lossy and low-bandwidth communication conditions. Existing dense BEV feature fusion methods depend on redundant BEV feature exchange, which is infeasible in low-bandwidth scenarios, while compact-communication methods aggressively compress messages but can hardly recover the missing feature content after packet loss. In this paper, we present LR-V2X, a loss-resilient, latent-space reconstruction framework that converts corrupted received latents (even under severe 90% packet loss) into a spatial prior and then reconstructs the missing BEV information from this informative prior and using ego context as condition. Notably, the model can be trained under complete communication conditions and can be directly applied to lossy conditions at test time, eliminating the need for training under numerous lossy conditions. Experiments on DAIR-V2X and V2XREAL show that LR-V2X delivers the strongest robustness under severe packet loss and preserves reliable collaboration as communication quality degrades. And it reduces communication overhead by $64\times$ compared to dense BEV feature fusion baselines. Code will be released at this https URL .

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.11264 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:55:07.289Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).