Computational techniques for processing and visualising very large datasets in 3D

我的研究计划的长期目标是能够创建计算环境，在这种环境中，人类可以直观地交互，通过适当的可视化更容易地理解复杂的行为和关系，并有效地协作以有效地完成现实世界的任务。基于传感器的数据采集、实时数据记录和来自复杂模拟的大量数据的快速发展导致数据大小的增长速度远远超过图形硬件加速或算法技术的进步。大多数3D处理、渲染和可视化技术不能扩展到这些数据大小，当然不能使用标准的商用硬件。新的计算技术可以有效地处理和实现非常大和复杂的数据集的3D可视化是必不可少的。拟议的研究直接建立在我之前在印度任职期间（直到2002年）的工作基础上，更具体地说，是建立在2002年加入Concordia后发起的研究项目和合作基础上的。在NSERC， CFI和其他机构的资助下，在过去的4年里，我在Concordia建立了“高级3D图形和视觉计算实验室”。该实验室目前配备了双头3D扫描仪，虚拟和增强现实设备，功能强大的3D图形集群和一套高端图形工作站。具体来说，我们将研究：1)与适用于大型数据集的宏观级特征/模式发现技术紧密耦合的可视化计算技术；这构成了本研究的核心，2)将原始3D扫描数据处理成易于用于游戏，电影，工程，安全等应用程序的格式的新方法3)在cpu和gpu（被视为协处理器）上使用实验室中的图形集群处理非常大的数据集的功能和数据分布。4)开发交互式和沉浸式系统，将这些技术整合到网络使用、生物信息学、患者教育和临床数据等多学科应用中。研究结果将极大地造福于图像研究社区以及加拿大的游戏、电影、医疗和工程行业。

The long term objective of my research programme is to be able to create computing environments in which humans can intuitively interact, comprehend complex behaviour and relationships more easily through suitable visualisations, and productively collaborate to accomplish real worlds tasks efficiently. Rapid advances in sensor based data acquisition, real time data logging and voluminous data from complex simulations have led to the situation that data sizes grow much faster than advances in graphics hardware acceleration or in algorithmic techniques. Most 3D processing, rendering and visualization techniques do not scale to these data sizes, certainly not with standard commodity hardware. New computational techniques that can efficiently process and enable 3D visualization of very large and complex data sets are essential. The proposed research directly builds on my previous work during my tenure in India (until 2002), and more specifically on the research programme and collaborations initiated after joining Concordia in 2002. With funding support from NSERC, CFI and other agencies, over the last 4 years I have set up the "Advanced 3D Graphics and Visual Computing Laboratory" at Concordia. This lab is currently equipped with a two-head 3D scanner, virtual and augmented reality devices, a powerful 3D graphics cluster and a suite of high end graphics workstations. Specifically, we shall investigate: 1)Computational techniques for closely coupling visualisation with macro-level feature/pattern discovery techniques applicable to large data sets; this forms the core of this research, 2)New methods of processing raw 3D scan data into a format  readily useful in applications such as gaming, cinema, engineering, security, etc. 3)Functionality and data distribution on CPUs and GPUs (looked upon as co-processors) for very large datasets using the graphics cluster in the lab. and 4)Development of interactive and immersive systems incorporating these techniques for multidisciplinary applications in web usage, bioinformatics, patient education and clinical data. The results would immensely benefit the graphics research community at large as well as Canadian industries of gaming, cinema, healthcare and engineering.