VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution#

Chendong Wang, Anlan Zhang, Yifan Yang, Lili Qiu, Yuqing Yang, XINYANG JIANG, Feng Qian, Suman Banerjee (networking-community author roster)

• Why: A bandwidth-vs-compute trade made explicit: ship downsampled volumetric (3D) video and reconstruct with lookup-table super-resolution at the receiver — 70% bandwidth reduction at line rate on commodity mobile devices. It's the classic edge/cdn rate-adaptation problem with a novel twist, from a networking research group.
• What: The first system enabling high-quality 3D super-resolution of volumetric streams on commodity mobile devices at line rate: LUT-based upscaling of pre-downsampled content, plus an adaptive bitrate loop that picks the downsampling rate based on network conditions. 70% bandwidth reduction, 36.7% QoE improvement, 8.4× faster 3D super-resolution with no quality loss. (from abstract only)
• How: Precomputed LUTs map low-resolution volumetric values to high-resolution reconstructions, replacing expensive on-device SR inference with fast table lookups; ABR logic sits on top, treating fidelity level like a video bitrate ladder.
• Where applicable: Volumetric/immersive media delivery over constrained last-mile networks to mobile receivers; the LUT-for-neural-SR substitution pattern generalizes to any receiver-side computationally-bound reconstruction. Evaluation is on mobile devices, not datacenter delivery.