{"ID":2828111,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15823","arxiv_id":"2512.15823","title":"Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications","abstract":"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.","short_abstract":"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 clou...","url_abs":"https://arxiv.org/abs/2512.15823","url_pdf":"https://arxiv.org/pdf/2512.15823v2","authors":"[\"Mohammad Waquas Usmani\",\"Sankalpa Timilsina\",\"Michael Zink\",\"Susmit Shannigrahi\"]","published":"2025-12-17T16:19:18Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.LG\",\"cs.MM\",\"eess.IV\"]","methods":"[]","has_code":false}