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arXiv Machine Learning By Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, Susmit Shannigrahi

Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications

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The paper presents a system for real‑time mixed reality that reduces bandwidth and encryption overhead by downsampling point cloud data at the server and partially encrypting it. The client decrypts the data and uses a machine‑learning super‑resolution model to reconstruct the full‑resolution point clouds. Experiments show a nearly linear decrease in bandwidth and latency, with the super‑resolution model achieving low reconstruction error and modest inference time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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