Research Profile: Jason Kimball

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|caption    =  Image courtesy Mark Duchaineau, LLNL
 
|caption    =  Image courtesy Mark Duchaineau, LLNL
 
|summary = Visualization algorithms for real time exploration of massive, time varying point cloud datasets produced by atomistic simulations. Challenges include developing level of detail algorithms for unstructured point data and representing sub-pixel features such as occlusion and intersections.  The massive size of these datasets (gigabytes per timestep) make simply loading and rendering an image a challenging task.  We are developing new algorithms for the efficient exploration of tera-scale datasets which contain millions to billions of objects as well as thousands of timesteps.  This project is in collaboration with Mark Duchaineau of Lawrence Livermore National Laboratories.
 
|summary = Visualization algorithms for real time exploration of massive, time varying point cloud datasets produced by atomistic simulations. Challenges include developing level of detail algorithms for unstructured point data and representing sub-pixel features such as occlusion and intersections.  The massive size of these datasets (gigabytes per timestep) make simply loading and rendering an image a challenging task.  We are developing new algorithms for the efficient exploration of tera-scale datasets which contain millions to billions of objects as well as thousands of timesteps.  This project is in collaboration with Mark Duchaineau of Lawrence Livermore National Laboratories.
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|reference = [[Research_Projects:_Terascale_Visualization |Read More]]
 
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|caption    =  A 720p60 HD video stream displayed on HIPerSpace that is responsive enough to play video games.
 
|caption    =  A 720p60 HD video stream displayed on HIPerSpace that is responsive enough to play video games.
 
|summary = Middleware for the acquisition, streaming and presentation of HD video and audio sources. This project ties together video capture hardware with a real-time texture compression library and multicast streaming protocol to delivery multiple HD resolution AV streams over gigabit networks with support for mobility -- which is useful for tiled displays. Input sources include HD video cameras, laptop/desktop machines and even video game consoles.
 
|summary = Middleware for the acquisition, streaming and presentation of HD video and audio sources. This project ties together video capture hardware with a real-time texture compression library and multicast streaming protocol to delivery multiple HD resolution AV streams over gigabit networks with support for mobility -- which is useful for tiled displays. Input sources include HD video cameras, laptop/desktop machines and even video game consoles.
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|reference = [[Research_Projects:_Video_Streaming |Read More]]
 
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Revision as of 11:51, 5 September 2008

Jason Kimball
jkimball@ucsd.edu

PhD Research Projects

Tera-Scale Atomistic Visualization
Visualization algorithms for real time exploration of massive, time varying point cloud datasets produced by atomistic simulations. Challenges include developing level of detail algorithms for unstructured point data and representing sub-pixel features such as occlusion and intersections. The massive size of these datasets (gigabytes per timestep) make simply loading and rendering an image a challenging task. We are developing new algorithms for the efficient exploration of tera-scale datasets which contain millions to billions of objects as well as thousands of timesteps. This project is in collaboration with Mark Duchaineau of Lawrence Livermore National Laboratories. (Read More)

Image courtesy Mark Duchaineau, LLNL

HD Teleconferencing and Video Mobility
Middleware for the acquisition, streaming and presentation of HD video and audio sources. This project ties together video capture hardware with a real-time texture compression library and multicast streaming protocol to delivery multiple HD resolution AV streams over gigabit networks with support for mobility -- which is useful for tiled displays. Input sources include HD video cameras, laptop/desktop machines and even video game consoles. (Read More)

A 720p60 HD video stream displayed on HIPerSpace that is responsive enough to play video games.

Dynamic IBR techniques for fixed cost stereoscopic support [1]
This project presents a GPU based implementation of an image-based rendering method for reducing the cost of stereoscopic rendering to the cost of rendering a single monoscopic image plus a smaller fixed cost. Our approach is to use the color and depth information from the rendered image of one eye to produce a reconstructed depthsprite which is rendered for the other eye. A GPU hardware accelerated technique for producing and rendering this depthsprite at rates above 60 Hz is presented. Our technique enables the real time stereoscopic display of complex and data intensive objects, which are currently constrained to monoscopic rendering technology. (Read More)

3D anaglyph rendered using fixed cost algorithm



Collaborative Visualization Environment [2] [3]
Imaging techniques such as MRI, fMRI, CT and PET have provided physicians and researchers with a means to acquire high-quality biomedical images as the foundation for the diagnosis and treatment of diseases. This research presents a framework for collaborative visualization of biomedical data-sets, supporting heterogeneous computational platforms and network configurations. The system provides the user with data visualization, annotation and the middleware to exchange the resulting visuals between all participants, in real-time. A resulting 2D visual provides a user specifiable high-resolution image slice, while a resulting 3D visual provides insight into the entire data set. To address the costly rendering of large-scale volumetric data, the visualization engine can distribute tasks over multiple render nodes. (Read More)

A picture of the CVE program with a human head CT scan.

Publications

  • [1] Kimball, J., Petrovic, V., and Kuester, F. (2006). Dynamic ibr techniques for fixed cost stereoscopic support. In Proceedings of the IEEE VR 2006
  • [2] He, Z., Kimball, J., and Kuester, F. (2005). Distributed and collaborative biomedical data exploration. Lecture Notes in Computer Science, 3804:271 – 278.
  • [3] Kuester, F., He, Z., Kimball, J., Quintos, M., and Tresens, M. A. (2005). Collaborative biomedical data exploration in distributed virtual environments. In et al., J. D. W., editor, Medicine Meets Virtual Reality 13, Studies in Health Technology and Informatics. IOS Press.
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