# Point Cloud in the Air

arXiv: https://arxiv.org/abs/2401.00658 · HTML: https://mlsanity.com/paper/2401.00658/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models](https://mlsanity.com/paper/2011.07590/) (2020)
- [Towards Holographic Video Communications: A Promising AI-driven Solution](https://mlsanity.com/paper/2210.06794/) (2022)
- [HoloCast: Graph Signal Processing for Graceful Point Cloud Delivery](https://mlsanity.com/paper/1903.03247/) (2019)
- [Over-the-Air Learning-based Geometry Point Cloud Transmission](https://mlsanity.com/paper/2306.08730/) (2023)

## Built on by (most cited first, 4 of 4)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Point Cloud Compression with Implicit Neural Representations: A Unified Framework](https://mlsanity.com/paper/2405.11493/) (2024)
- [Implicit Neural Compression of Point Clouds](https://mlsanity.com/paper/2412.10433/) (2024)
- [Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness](https://mlsanity.com/paper/2603.13560/) (2026)

## 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.