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

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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.