Title page for ETD etd-0702102-172401


Type of Document Dissertation
Author Liu, Xinlian
URN etd-0702102-172401
Title Fast Scalable Visualization Techniques for Interactive Billion-Particle Walkthrough
Degree Doctor of Philosophy (Ph.D.)
Department Computer Science
Advisory Committee
Advisor Name Title
Aiichiro Nakano Committee Chair
Jerry Trahan Committee Member
John Tyler Committee Member
S. S. Iyengar Committee Member
Rajiv Kalia Dean's Representative
Keywords
  • virtual reality
  • walkthrough
  • interactive
  • scalable
  • visualization
Date of Defense 2002-06-14
Availability unrestricted
Abstract
This research develops a comprehensive framework for interactive walkthrough involving one billion particles in an immersive virtual environment to enable interrogative visualization of large atomistic simulation data. As a mixture of scientific and engineering approaches, the framework is based on four key techniques: adaptive data compression based on space-filling curves, octree-based visibility and occlusion culling, predictive caching based on machine learning, and scalable data reduction based on parallel and distributed processing. In terms of parallel rendering, this system combines functional parallelism, data parallelism, and temporal parallelism to improve interactivity.

The visualization framework will be applicable not only to material simulation, but also to computational biology, applied mathematics, mechanical engineering, and nanotechnology, etc.

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