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arXiv Computer Vision By Kang Yang, Tianci Bu, Peng Wang, Deying Li, Yongcai Wang

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

Read the original on arXiv Computer Vision →

LR‑V2X is a loss‑resilient collaborative perception framework for vehicular networks that reconstructs missing bird‑view (BEV) features from corrupted latent representations, even under severe packet loss. It converts corrupted latents into a spatial prior and uses ego‑vehicle context to recover BEV information, requiring only training under full‑communication conditions. Experiments on DAIR‑V2X and V2XREAL demonstrate that LR‑V2X maintains robust collaboration while reducing communication overhead by 64× compared to dense BEV fusion methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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