用量化点云索引通信，大幅降带宽还抗丢包。
Send Less, Perceive More: Masked Quantized Point Cloud Communication for Loss-Tolerant Collaborative Perception
- 用共享码本传输点云索引，不传特征，节省带宽。
- 在模拟和真实数据集上均实现更高精度与更强抗丢包能力。
- 适合车联网等低带宽、高容错的协同感知场景。
协同感知使联网车辆通过共享传感信息克服遮挡和视角局限。但现有方法在严格带宽约束下难以保持高精度，且对随机传输丢包极为敏感。本文提出 QPoint2Comm，一种量化点云通信框架，显著降低带宽同时保留高保真3D信息。不同于传输中间特征，该框架直接使用共享码本发送量化点云索引，实现高效重建，带宽低于基于特征的方法。为提升对丢包的鲁棒性，采用掩码训练策略模拟随机丢包，使模型在严重传输故障下仍保持强性能。此外，引入级联注意力融合模块以增强多车信息整合。大量实验在模拟与真实数据集上表明，QPoint2Comm 在精度、通信效率及抗丢包能力方面均达到新基准。
原文摘要 · Abstract (English)
Collaborative perception allows connected vehicles to overcome occlusions and limited viewpoints by sharing sensory information. However, existing approaches struggle to achieve high accuracy under strict bandwidth constraints and remain highly vulnerable to random transmission packet loss. We introduce QPoint2Comm, a quantized point-cloud communication framework that dramatically reduces bandwidth while preserving high-fidelity 3D information. Instead of transmitting intermediate features, QPoint2Comm directly communicates quantized point-cloud indices using a shared codebook, enabling efficient reconstruction with lower bandwidth than feature-based methods. To ensure robustness to possible communication packet loss, we employ a masked training strategy that simulates random packet loss, allowing the model to maintain strong performance even under severe transmission failures. In addition, a cascade attention fusion module is proposed to enhance multi-vehicle information integration. Extensive experiments on both simulated and real-world datasets demonstrate that QPoint2Comm sets a new state of the art in accuracy, communication efficiency, and resilience to packet loss.
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