US Ignite: Focus Area 1: Predictable Wireless Networking and Collaborative 3D Reconstruction for Real-Time Augmented Vision

US Ignite：重点领域 1：用于实时增强视觉的可预测无线网络和协作 3D 重建

基本信息

批准号：
1647200
负责人：
Hongwei Zhang
金额：
$ 60万
依托单位：
Wayne State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-10-01 至 2018-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647200&HistoricalAwards=false
关键词：
US Ignite Focus Area Predictable

项目摘要

Eliminating the line-of-sight constraint of human vision and machine vision, the developed network systems foundations of predictable wireless networked and 3D reconstruction will enable 'see-through vision' which will transform the ways humans and engineered systems interact with environments and thus have far-reaching impact on domains such as road transportation, public safety, and disaster response. This project develops the network systems foundation for a vehicle equipped with sensors and an augmented reality display to indicate the presence of other nearby vehicles hidden by obstacles. In collaboration with Wayne State University (WSU) police and Ford Research and leveraging the WSU living lab and the OpenXC open-source platform for connected vehicles, the project will take an integrated approach to the research, deployment, and dissemination of the wireless network systems for see-through vision. This project proposes a cross-layer framework for addressing physical-domain uncertainties and the interdependencies between wireless networking and 3D reconstruction, and it develops novel algorithms for predictable wireless networking and real-time wireless networked 3D reconstruction. Using the developed network system, this project will develop a see-through vision application for human-driving. The wireless networked see-through vision system will be deployed in the WSU police patrol vehicles, and the project team will outreach to the Detroit and State of Michigan police as well as open-source communities for broad adoption and deployment of the see-through vision system.With the bold objective of eliminating the line-of-sight constraint of human vision and machine vision, this project addresses wireless networking and 3D reconstruction challenges in a holistic cross-layer framework. By integrating research investigation with systems development and deployment, this project will make the following significant contributions: 1) Effectively leveraging multi-scale physical structures of traffic flows, the multi-scale approach to resource management in vehicular wireless networks not only ensures predictable vehicular wireless networking, it also transforms fundamental challenges of vehicular networks to ones similar to those of mostly-immobile networks, thus enabling the exploration of fundamental, generically-applicable principles and mechanisms for predictable wireless networking; 2) the multi-scale approach to joint scheduling, channel assignment, power control, and rate control enables predictable control of per-packet transmission reliability in the presence of fast-varying network and environmental conditions such as wireless channel attenuation, internal and external interference, data traffic dynamics, and vehicle mobility; 3) the real-time scheduling algorithm enables controllable exploration of real-time capacity regions for system-level optimization; 4) the collaborative 3D reconstruction model integrates visual sensors in a divide-and-conquer fashion, and it enhances the capability of networked vision as well as its robustness to physical uncertainties; 5) the co-design of collaborative 3D reconstruction and wireless networking permits adaptive communication capacity allocation to optimize the quality of 3D reconstruction; 6) the attention-aware see-through vision application creates a new research field of vision augmentation by uniquely integrating computer vision and computer graphics research and by proposing a practical solution for displaying augmented 3D vision.

消除了人类视野和机器视觉的视线限制，开发的可预测无线网络和3D重建的网络系统基础将使“透明视觉”能够改变人类和工程系统与环境的交互方式，从而对诸如公路运输，公共安全以及灾难响应等领域的影响深远。该项目为配备传感器的车辆和增强现实显示器开发了网络系统基金会，以表明附近的其他车辆被障碍物隐藏。与韦恩州立大学（WSU）警察和福特研究，并利用WSU Living Lab和OpenXC开源平台用于连接的车辆，该项目将采用综合方法来研究无线网络系统的研究，部署和传播无线网络系统。该项目提出了一个跨层框架，用于解决物理域的不确定性以及无线网络和3D重建之间的相互依赖性，并为可预测的无线网络和实时无线网络3D重建提供了新颖的算法。使用开发的网络系统，该项目将为人类驾驶开发透明视觉应用程序。无线网络的透明视觉系统将部署在WSU警察巡逻车中，项目团队将向底特律和密歇根州警察局以及开源社区以及开源社区进行宣传，以广泛采用和部署透明视觉系统。在无线电话中，挑战人类视野的大胆构造，并挑战了人类视觉的构造，并无需进行挑战，并挑战了3号项目，并无需挑战。跨层框架。通过将研究调查与系统开发和部署整合在一起，该项目将做出以下重要贡献：1）有效利用交通流量的多尺度物理结构，在车辆无线网络中进行资源管理的多尺度方法，不仅可以确保可预测的无线网络，因此它还可以改变与那些网络相似的网络的基本挑战，从而使人们对那些类似的网络进行了启动的挑战。可预测的无线网络的基本，一般性的原则和机制； 2）在存在快速变化的网络和环境条件（例如无线通道衰减，内部和外部干扰，数据流量动态和车辆移动性）的情况下，可以预见到可预测的每包装传输可靠性的多尺度方法，可预测地控制每包传输可靠性； 3）实时调度算法可控制实时容量区域的系统级优化； 4）协作3D重建模型以分裂和互动的方式集成了视觉传感器，并增强了网络视觉的能力及其对物理不确定性的鲁棒性； 5）协作3D重建和无线网络的共同设计允许自适应通信能力分配以优化3D重建的质量； 6）引人注目的透明视觉应用程序通过唯一整合计算机视觉和计算机图形研究并提出一种实用解决方案来展示增强的3D视觉，从而创建了一个新的视觉增强研究领域。