DeformStream: Deformation-based Adaptive Volumetric Video Streaming
Boyan Li, Yongting Chen, Dayou Zhang, Fangxin Wang

TL;DR
DeformStream introduces a deformation-based adaptive streaming framework for volumetric videos that reduces bandwidth and maintains visual quality by leveraging mesh deformability and optimizing frame reconstruction.
Contribution
It presents a novel deformation-based streaming framework with a QoE model and dynamic programming for bandwidth-quality trade-offs in real-time volumetric video.
Findings
Outperforms existing mesh-based systems in bandwidth efficiency
Maintains high visual quality with reduced bandwidth usage
Effectively adapts to fluctuating network conditions
Abstract
Volumetric video streaming offers immersive 3D experiences but faces significant challenges due to high bandwidth requirements and latency issues in transmitting detailed content in real time. Traditional methods like point cloud streaming compromise visual quality when zoomed in, and neural rendering techniques are too computationally intensive for real-time use. Though mesh-based streaming stands out by preserving surface detail and connectivity, offering a more refined representation for 3D content, traditional mesh streaming methods typically transmit data on a per-frame basis, failing to take full advantage of temporal redundancies across frames. This results in inefficient bandwidth usage and poor adaptability to fluctuating network conditions. We introduce Deformation-based Adaptive Volumetric Video Streaming, a novel framework that enhances volumetric video streaming performance…
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Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Multimedia Communication and Technology
