Dynamic Adaptive Point Cloud Streaming

TL;DR

This paper introduces DASH-PC, a dynamic adaptive streaming system for high-quality point clouds that reduces bandwidth usage while maintaining visual quality through view-aware and semantic-aware adaptations.

Contribution

It extends DASH to point cloud streaming with new thinning methods and a specialized manifest, enabling efficient, adaptive, and view-aware streaming of dense 3D data.

Findings

Significant bandwidth reduction achieved with minor quality loss.

View-aware adaptation improves visual quality for human perception.

Proposed system outperforms baseline static streaming methods.

Abstract

High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds. In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.