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

• 批准号: 1821962
• 负责人: Hongwei Zhang
• 金额: $ 55.04万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-31 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821962&HistoricalAwards=false
• 关键词: US; Ignite; Focus; Area; Predictable

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.

消除人类视觉和机器视觉的视线限制，可预测无线网络和 3D 重建的发达网络系统基础将实现“透视视觉”，这将改变人类和工程系统与环境交互的方式，从而对道路交通、公共安全和灾害应对等领域产生深远影响。该项目为配备传感器和增强现实显示器的车辆开发网络系统基础，以指示附近被障碍物隐藏的其他车辆的存在。 该项目将与韦恩州立大学 (WSU) 警方和福特研究中心合作，利用 WSU 生活实验室和 OpenXC 联网车辆开源平台，采用综合方法来研究、部署和传播无线网络系统实现透视视觉。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，并开发了用于可预测无线网络和实时无线网络 3D 重建的新颖算法。利用已开发的网络系统，该项目将开发用于人类驾驶的透视视觉应用程序。无线网络透视视觉系统将部署在 WSU 警察巡逻车中，项目团队将联系底特律和密歇根州警察以及开源社区，以广泛采用和部署透视视觉该项目的大胆目标是消除人类视觉和机器视觉的视线限制，在整体跨层框架中解决无线网络和 3D 重建挑战。通过将研究调查与系统开发和部署相结合，该项目将做出以下重大贡献： 1）有效利用交通流的多尺度物理结构，车载无线网络中资源管理的多尺度方法不仅确保了可预测的车载无线网络它还将车载网络的基本挑战转变为与大多数固定网络类似的挑战，从而能够探索可预测无线网络的基本、普遍适用的原则和机制； 2) 联合调度、信道分配、功率控制和速率控制的多尺度方法能够在存在快速变化的网络和环境条件（例如无线信道衰减、内部和外部干扰）的情况下对每个数据包的传输可靠性进行可预测的控制、数据流量动态和车辆移动性； 3）实时调度算法能够实现实时容量区域的可控探索，以实现系统级优化； 4）协作3D重建模型以分而治之的方式集成视觉传感器，增强了网络视觉的能力及其对物理不确定性的鲁棒性； 5）协同3D重建和无线网络的协同设计允许自适应通信容量分配以优化3D重建的质量； 6）注意力感知透视视觉应用通过独特地整合计算机视觉和计算机图形学研究并提出显示增强3D视觉的实用解决方案，开创了视觉增强的新研究领域。

项目成果

Surface Registration with Eigenvalues and Eigenvectors

具有特征值和特征向量的表面配准

• DOI: 10.1109/tvcg.2019.2915567
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Hamidian, Hajar;Zhong, Zichun;Fotouhi, Farshad;Hua, Jing
• 通讯作者: Hua, Jing

Scheduling with Predictable Link Reliability for Wireless Networked Control

具有可预测链路可靠性的无线网络控制调度

• 发表时间: 2017-10
• 期刊: IEEE transactions on wireless communications
• 影响因子: 10.4
• 作者: Zhang, H.;Liu, X.;Li, C.;Chen, Y.;Che, Xin;Lin, Feng;Wang, Le Yi;Yin, George
• 通讯作者: Yin, George

Distributed Scheduling and Power Control for Predictable IoT Communication Reliability

分布式调度和功率控制可实现可预测的物联网通信可靠性

• 发表时间: 2018-04
• 期刊: IEEE International Conference on Communications (ICC
• 作者: Wang, Ling;Zhang, Hongwei;Ren, Pengfei
• 通讯作者: Ren, Pengfei

Cyber-Physical Scheduling for Predictable Reliability of Inter-Vehicle Communications

用于车辆间通信可预测可靠性的网络物理调度

• DOI: 10.1109/iotdi.2018.00035
• 发表时间: 2018-04
• 期刊: 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI
• 作者: Li, Chuan;Zhang, Hongwei;Rao, Jayanthi;Wang, Le Yi;Yin, George
• 通讯作者: Yin, George

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

A-CNN：点云上的环形卷积神经网络

• DOI: 10.1109/cvpr.2019.00760
• 发表时间: 2019-04-16
• 期刊: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Artem Komarichev;Z. Zhong;Jing Hua
• 通讯作者: Jing Hua