The paper investigates the security of a selective coordinate encryption scheme for point clouds used in volumetric video. By training PointNet and Random Forest models on partially encrypted data, the authors show that fully encrypting all X coordinates is difficult to reconstruct, whereas encrypting every second X coordinate allows accurate recovery of the hidden values. The study highlights that the encryption granularity critically determines the vulnerability of the scheme.
By Mohammad Waquas Usmani, Susmit Shannigrahi, Michael Zink
ResStreamGS introduces a residual‑sensitive dual‑stream framework for mobile volumetric video streaming that distinguishes temporally stable Gaussians from residual‑sensitive ones. Stable Gaussians are transmitted once per Group of Pictures, while only the residual‑sensitive Gaussians are included in the frame‑wise video stream, reducing bandwidth usage. Experiments on complex full‑scene videos show a 7.51–8.35 dB PSNR improvement over a V³‑style baseline, a payload reduction of up to 56.5%, and real‑time performance of 30.883 FPS on a Qualcomm Snapdragon 778G.
By Haoran Liu, Di Wu, Wang Luo, Hengyuan Na, Miao Hu, Guocong Quan
arXiv:2607.10495v2 Announce Type: replace
Abstract: Recent Video Super-Resolution (VSR) methods rely heavily on transformers and explicit optical flow, creating computational overhead and custom oper...
By Filip Pawlicki, Marcel Ka\'ndu{\l}a, Marcin Pucek, Kamil Dobies
arXiv:2608.11645v2 Announce Type: replace
Abstract: Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame...
By Hossein Khalili (UCLA), Philip Do (UCLA), Alexander Vilesov (UCLA), Achuta Kadambi (UCLA), Kittipat Apicharttrisorn (Nokia Bell Labs), Nader Sehatbakhsh (UCLA)
arXiv:2608. 19639v1 Announce Type: new Abstract: Streaming reconstruction of Free-Viewpoint Videos (FVVs) supports immersive Internet of Things (IoT) services, such as telepresence and digital twin visualization.
By Yiwei Li, Jiannong Cao, Weixun Gao, Rui Cao, Songye Zhu, Yinfeng Cao, Mingjin Zhang
arXiv:2607. 10098v1 Announce Type: cross Abstract: Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent connections, making continuous data transmission to the cloud impractical.
By Ziyang Zhang, Jie Liu, Luca Mottola
arXiv:2503.19634v2 Announce Type: replace
Abstract: Burst image super-resolution (BISR) reconstructs a high-resolution keyframe by aggregating complementary sub-pixel evidence from a short burst of l...
By Ozan Unal, Steven Marty, Dengxin Dai
arXiv:2610.09720v1 Announce Type: new
Abstract: Online reconstruction of dynamic 4D scenes from long, unposed streaming videos requires both continuous processing and photorealistic rendering, which...
By Dingwei Xian, Xiaoyu Zhou, Yajiao Xiong, Yongtao Wang, Ming-Hsuan Yang
arXiv:2607. 20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction.
By Renbiao Jin, Mingxin Yang, Yutian Chen, Junhao Zhuang, Xin Cai, Mulin Yu, Linning Xu, Wenxian Yu, Danping Zou, Shi Guo, Tianfan Xue
RelayVSR introduces a streaming video super‑resolution framework that combines a large generative model, which produces reference latents for sparse keyframes, with a lightweight Dual‑Memory Video Transformer that super‑resolves every frame using these references and low‑resolution input. The method employs Video‑Aware Reference Optimization (VARO), a reinforcement‑learning strategy that optimizes both system‑level video quality and reference‑level keyframe fidelity, outperforming direct joint training. On 1080p video, RelayVSR achieves 29.29 FPS with modest GPU memory usage, significantly faster and more efficient than the FlashVSR‑Tiny baseline.
By Xijun Wang, Xin Li, Zirui Lang, Suhang Yao, Haoran Li, Zhibo Chen
Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders.
arXiv:2609.17230v1 Announce Type: new
Abstract: Streaming 3D reconstruction demands both speed and temporal fidelity, goals that existing methods undermine by updating every Gaussian every frame, eve...
By Idil Sulo, Alexey Supikov, Ilke Demir, Sainan Liu