# HoloCast: Graph Signal Processing for Graceful Point Cloud Delivery

## TL;DR

HoloCast introduces a novel graph signal processing approach for point cloud delivery that ensures graceful quality improvement over wireless channels, avoiding the cliff effect of traditional digital methods.

## Contribution

It applies graph Fourier transform to directly transmit 3D point cloud data, enabling more robust and smooth quality scaling in wireless environments.

## Key findings

- Outperforms digital methods in noisy wireless channels
- Provides graceful quality improvement with channel quality
- Handles non-ordered, non-uniform 3D signals effectively

## Abstract

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multi-dimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.