用机器学习超分辨率技术，实现低延迟的点云加密传输。
Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications
- 服务器下采样+部分加密，客户端用模型重建高清点云。
- 带宽与延迟近线性下降，重建误差小，推理时间短。
- 适合实时混合现实应用，兼顾安全与性能。
沉浸式格式如360°和6DoF点云视频需要高带宽和低延迟，对实时AR/VR流媒体构成挑战。本文聚焦降低带宽消耗和加解密延迟，这两个主要贡献于整体延迟的因素。我们设计了一套系统：在源服务器端对点云内容进行下采样并实施部分加密；在客户端解密后，使用基于机器学习的超分辨率模型进行上采样重建。评估表明，随着下采样分辨率降低，带宽与延迟近乎线性减少，且超分辨率模型能以极小误差有效重建原始全分辨率点云，同时保持适度的推理时间。
原文摘要 · Abstract (English)
Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time.
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