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Added by
Tarek El-Ganainy
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
The recent rise of interest in Virtual Reality (VR) came with the availability of commodity commercial VR prod- ucts, such as the Head Mounted Displays (HMD) created by Oculus and other vendors. To accelerate the user adoption of VR headsets, content providers should focus on producing high quality immersive content for these devices. Similarly, multimedia streaming service providers should enable the means to stream 360 VR content on their platforms. In this study, we try to cover different aspects related to VR content representation, streaming, and quality assessment that will help establishing the basic knowledge of how to build a VR streaming system.
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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 culling them or reducing their level of detail depending on their relation to the users view frustum and distance to the user. Our system reduces latency by introducing a window-based buffer, which in contrast to a queue-based buffer allows insertions near the head of the buffer rather than only at the tail of the buffer, to respond quickly to user interaction. To allocate bits between different tiles across multiple objects, we introduce a simple greedy yet provably optimal algorithm for rate-utility optimization. We introduce utility measures based not only on the underlying quality of the representation, but on the level of detail relative to the users viewpoint and device resolution. Simulation results show that the proposed algorithm provides superior quality compared to existing video-streaming approaches adapted to hologram streaming, in terms of utility and user experience over variable, throughput-constrained networks.
The panoramic video is widely used to build virtual reality (VR) and is expected to be one of the next generation Killer-Apps. Transmitting panoramic VR videos is a challenging task because of two problems: 1) panoramic VR videos are typically much larger than normal videos but they need to be transmitted with limited bandwidth in mobile networks. 2) high-resolution and fluent views should be provided to guarantee a superior user experience and avoid side-effects such as dizziness and nausea. To address these two problems, we propose a novel interactive streaming technology, namely Focus-based Interactive Streaming Framework (FISF). FISF consists of three parts: 1) we use the classic clustering algorithm DBSCAN to analyze real user data for Video Focus Detection (VFD); 2) we propose a Focus-based Interactive Streaming Technology (FIST), including a static version and a dynamic version; 3) we propose two optimization methods: focus merging and prefetch strategy. Experimental results show that FISF significantly outperforms the state-of-the-art. The paper is submitted to Sigcomm 2017, VR/AR Network on 31 Mar 2017 at 10:44:04am EDT.
Adaptive bitrate (ABR) streaming is the de facto solution for achieving smooth viewing experiences under unstable network conditions. However, most of the existing rate adaptation approaches for ABR are content-agnostic, without considering the semantic information of the video content. Nevertheless, semantic information largely determines the informativeness and interestingness of the video content, and consequently affects the QoE for video streaming. One common case is that the user may expect higher quality for the parts of video content that are more interesting or informative so as to reduce video distortion and information loss, given that the overall bitrate budgets are limited. This creates two main challenges for such a problem: First, how to determine which parts of the video content are more interesting? Second, how to allocate bitrate budgets for different parts of the video content with different significances? To address these challenges, we propose a Content-of-Interest (CoI) based rate adaptation scheme for ABR. We first design a deep learning approach for recognizing the interestingness of the video content, and then design a Deep Q-Network (DQN) approach for rate adaptation by incorporating video interestingness information. The experimental results show that our method can recognize video interestingness precisely, and the bitrate allocation for ABR can be aligned with the interestingness of video content while not compromising the performances on objective QoE metrics.
Reconstructing 3D models from large, dense point clouds is critical to enable Virtual Reality (VR) as a platform for entertainment, education, and heritage preservation. Existing 3D reconstruction systems inevitably make trade-offs between three conflicting goals: the efficiency of reconstruction (e.g., time and memory requirements), the visual quality of the constructed scene, and the rendering speed on the VR device. This paper proposes a reconstruction system that simultaneously meets all three goals. The key idea is to avoid the resource-demanding process of reconstructing a high-polygon mesh altogether. Instead, we propose to directly transfer details from the original point cloud to a low polygon mesh, which significantly reduces the reconstruction time and cost, preserves the scene details, and enables real-time rendering on mobile VR devices. While our technique is general, we demonstrate it in reconstructing cultural heritage sites. We for the first time digitally reconstruct the Elmina Castle, a UNESCO world heritage site at Ghana, from billions of laser-scanned points. The reconstruction process executes on low-end desktop systems without requiring high processing power, making it accessible to the broad community. The reconstructed scenes render on Oculus Go in 60 FPS, providing a real-time VR experience with high visual quality. Our project is part of the Digital Elmina effort (http://digitalelmina.org/) between University of Rochester and University of Ghana.
Virtual Reality (VR) games that feature physical activities have been shown to increase players motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the games challenge according to the players capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the players current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame. As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the players capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.
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