Volumetric video is an emerging technology for immersive representation of 3D spaces that captures objects from all directions using multiple cameras and creates a dynamic 3D model of the scene. However, processing volumetric content requires high amounts of processing power and is still a very demanding task for today's mobile devices. To mitigate this, we propose a volumetric video streaming system that offloads the rendering to a powerful cloud/edge server and only sends the rendered 2D view to the client instead of the full volumetric content. We use 6DoF head movement prediction techniques, WebRTC protocol and hardware video encoding to ensure low-latency in different parts of the processing chain. We demonstrate our system using both a browser-based client and a Microsoft HoloLens client. Our application contains generic interfaces that allow for easy deployment of various augmented/mixed reality clients using the same server implementation.
The rise of capturing systems for objects and scenes in 3D with increased fidelity and immersion has led to the popularity of volumetric video contents that can be seen from any position and angle in 6 degrees of freedom navigation. Such contents need large volumes of data to accurately represent the real world. Thus, novel optimization solutions and delivery systems are needed to enable volumetric video streaming over bandwidth-limited networks. In this chapter, we discuss theoretical approaches to volumetric video streaming optimization, through compression solutions, as well as network and …
Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video requires lots of computational power which is challenging especially for mobile devices. To mitigate this, we developed a streaming system that renders a 2D view from the volumetric video at a cloud server and streams a 2D video stream to the client. However, such network-based processing increases the motion-to-photon (M2P) latency due to the additional network and processing delays. In order to compensate the added latency, prediction of the future user pose is nec…
Volumetric media, popularly known as holograms, need to be delivered to users using both on-demand and live streaming, for new augmented reality (AR) and virtual reality (VR) experiences. As in video streaming, hologram streaming must support network adaptivity and fast startup, but must also moderate large bandwidths, multiple simultaneously streaming objects, and frequent user interaction, which requires low delay. In this paper, we introduce the first system to our knowledge designed specifically for streaming volumetric media. The system reduces bandwidth by introducing 3D tiles, and culli…
CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting
Simon Fraser University（西蒙弗雷泽大学）；Jiangxing Intelligence Inc.（江行智能有限公司）；McGill University（麦吉尔大学）；Nankai University（南开大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Fuzhou University（福州大学）；Southeast University（东南大学）
Viewport Prediction for Volumetric Video Streaming by Exploring Video Saliency and Trajectory Information
School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）；The University of Electro-Communications（电子通信大学）；Aarhus University（奥胡斯大学）；School of Electrical Engineering and Automation, Hefei University of Technology（电气工程与自动化学院，合肥工业大学）；Engineering Technology Research Center of Industrial Automation of Anhui Province（安徽省工业自动化工程技术研究中心）；School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）
Volumetric video allows viewers to experience highly-realistic 3D content with six degrees of freedom in mixed reality (MR) environments. Rendering complex volumetric videos can require a prohibitively high amount of computational power for mobile devices. A promising technique to reduce the computational burden on mobile devices is to perform the rendering at a cloud server. However, cloud-based rendering systems suffer from an increased interaction (motion-to-photon) latency that may cause registration errors in MR environments. One way of reducing the effective latency is to predict the vie…
School of Electrical Engineering and Automation, Hefei University of Technology, and Engineering Technology Research Center of Industrial Automation Anhui Province, Hefei, Anhui 230009, China（合肥工业大学电气与自动化学院，安徽省工业自动化工程技术研究中心）；The University of Electro-Communications, Tokyo, Japan（东京电讯大学）；School of Computer Science and Technology, University of Science and Technology of China, Hefei, China（中国科学技术大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark（哥本哈根大学电子与计算机工程系）；Department of Computer Science and Electrical Engineering, University of Missouri–Kansas City, Kansas, USA（密苏里大学- Kansas 城市分校计算机科学与电气工程系）；School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China（合肥工业大学计算机科学与信息工程学院）
CPSL: Representing Volumetric Video via Content-Promoted Scene Layers
McGill University（麦吉尔大学）；University of Southern California（南加州大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
