mlsanityData to 7 Oct 2026

Point Cloud in the Air

arXiv 2401.00658 ↗First posted 1 Jan 2024cs.IT cs.LG cs.MMcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 1 Jan 2024.

Builds on

5 of 16 references.

References · 5

  1. 2020Deep Learning Enabled Semantic Communication Systemscited by 282
  2. 2020MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Modelscited by 42
  3. 2022Towards Holographic Video Communications: A Promising AI-driven Solutioncited by 5
  4. 2019HoloCast: Graph Signal Processing for Graceful Point Cloud Deliverycited by 2
  5. 2023Over-the-Air Learning-based Geometry Point Cloud Transmissioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.