CCSS: Collaborative Research: Ubiquitous Sensing for VR/AR Immersive Communication: A Machine Learning Perspective

虚拟和增强现实系统包括多视角摄像头传感器，可以从多个角度捕捉场景。然后使用捕获的数据在用户的头盔显示器上构建场景的身临其境的表示。这些系统将支持和加强许多重要的应用，例如，大规模基础设施的检查、历史遗迹的存档、搜索和救援、灾害应对、军事侦察、自然资源管理和身临其境的远程呈现。然而，由于虚拟/增强现实沉浸式通信的新兴性质，目前仅限于以离线捕获/计算机生成的内容、演播室类型的设置和高端工作站为特色的游戏或娱乐演示，以维持其高数据/计算工作负载。此外，对于所需的信号采集密度和传感器在空间和时间上的位置、捕获场景的动态(运动、几何和纹理)、可用的网络和系统资源以及提供的沉浸质量之间的基本权衡，人们几乎不了解。这使得现有的解决方案不适用于在带宽和能源受限的远程传感器上部署。该项目通过在多视点时空传感和信号表示、延迟敏感通信和机器学习的交叉点上进行严格的分析和协调的算法和应用程序进步来解决这些挑战。教育和推广活动将使学生沉浸在视觉传感、无线通信和机器学习等令人兴奋的领域，并将吸引从K-12到本科水平的未被充分代表的学生。该项目的目标是在有限的采样和通信资源下，使用多个相机传感器有效地捕捉远程环境，并具有尽可能高的重建质量。这是通过四个相互关联的研究任务实现的：(I)分析确定传感器位置和采样率的最优时空采样策略，以最小化远程场景的重建误差；(Ii)设计最优信号表示方法，根据分配的采样率在空间和时间上联合嵌入采样数据；(Iii)基于谱图理论设计在线学习采样策略，在缺乏先验场景视点信号知识的情况下，在探索新的传感器位置的同时采取采样行动；以及(Vi)设计计算高效的自组织强化学习方法，该方法允许无线传感器计算满足覆盖的虚拟/增强现实应用的低等待时间要求的最佳传输调度策略，同时保存其可用能量。将进行整合、实验和原型制作活动，以在真实世界环境中评估和验证研究进展。这些技术进步将使变革性影响的各种应用成为可能。

Virtual and augmented reality systems comprise multi-view camera sensors that capture a scene from multiple perspectives. The captured data is then used to construct an immersive representation of the scene on the user's head mounted display. Such systems are poised to enable and enhance numerous important applications, e.g., inspection of large-scale infrastructure, archival of historical sites, search and rescue, disaster response, military reconnaissance, natural resource management, and immersive telepresence. However, due to its emerging nature, virtual/augmented reality immersive communication is presently limited to gaming or entertainment demonstrations featuring off-line captured/computer-generated content, studio-type settings, and high-end workstations to sustain its high data/computing workload. Moreover, there is little understanding of the fundamental trade-offs between the required signal acquisition density and sensor locations across space and time, the dynamics of the captured scene (motion, geometry, and textures), the available network and system resources, and the delivered immersion quality. This renders existing solutions impractical for deployment on bandwidth and energy constrained remote sensors. The project addresses these challenges via rigorous analysis and concerted algorithmic and application advances at the intersection of multi-view space-time sensing and signal representation, delay-sensitive communication, and machine learning. Education and outreach activities will immerse students in the exciting areas of visual sensing, wireless communications, and machine learning, and will engage underrepresented students spanning K-12 through undergraduate levels.The objective of this project is to efficiently capture a remote environment using multiple camera sensors with the highest possible reconstruction quality under limited sampling and communication resources. This is achieved through four interrelated research tasks: (i) analysis of optimal space-time sampling policies that determine the sensors' locations and sampling rates to minimize the remote scene's reconstruction error; (ii) design of optimal signal representation methods that embed the sampled data jointly across space and time according to the allocated sampling rates; (iii) design of online learning sampling policies based on spectral graph theory that take sampling actions while exploring new sensor locations in the absence of a priori scene viewpoint signal knowledge; and (vi) design of computationally efficient self-organizing reinforcement learning methods that allow the wireless sensors to compute optimal transmission scheduling policies that meet the low-latency requirements of the overlaying virtual/augmented reality application while conserving their available energy. Integration, experimentation, and prototyping activities will be conducted to asses and validate the research advances in real-world settings. These technical advances will enable diverse applications of transformative impact.