Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission.

翻译：点云（PC）的采集与处理是众多依赖3D空间数据的新兴应用（如机器人导航、自动驾驶及增强现实）的关键实现技术。多数场景下，由遥感器获取的点云数据必须传输至边缘服务器以完成融合、分割或推理等任务。点云的无线传输不仅会加剧已显拥挤的无线频谱负担，更因点云数据固有的不规则与非结构化特性面临特殊挑战。本文系统阐明了这些技术挑战，在客观指出现有解决方案固有局限性的基础上，对现有方法进行了全面评述。针对这些复杂问题，我们提出了涵盖先进技术、混合方案与分布式数据聚合方法在内的四类实用解决方案框架。通过此项研究，我们旨在为高效、可靠且低延迟的无线点云传输指明发展方向。