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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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Isosurface extraction and view-dependent filtering from time-varying fields using Persistent Time-Octree (PTOT).

Cong Wang1, Yi-Jen Chiang

  • 1Polytechnic Institute of New York University, Brooklyn, NY, USA. cwang05@students.poly.edu

IEEE Transactions on Visualization and Computer Graphics
|October 17, 2009
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Summary

We introduce a new Persistent Time-Octree (PTOT) for efficient isosurface extraction and view-dependent filtering of large time-varying scientific data. This method optimizes searching and reduces computational costs for complex datasets.

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Area of Science:

  • Computer Science
  • Scientific Visualization
  • Data Management

Background:

  • Isosurface extraction and view-dependent filtering are crucial for analyzing large scientific datasets.
  • Existing methods like Persistent Octree (POT) are limited to static data, while 4D-POT for time-varying data has suboptimal search efficiency.
  • Integrating time-domain slicing with view-dependent filtering for time-varying fields remains a challenge.

Purpose of the Study:

  • To develop a novel indexing structure, the Persistent Time-Octree (PTOT), for efficient isosurface extraction and view-dependent filtering of large time-varying fields.
  • To achieve output-sensitive and asymptotically optimal searching for active cells in time-varying data.
  • To enable efficient processing of datasets larger than main memory using GPUs and out-of-core techniques.

Main Methods:

  • Development of the Persistent Time-Octree (PTOT) indexing structure.
  • Implementation of an out-of-core scheme integrating PTOT with implicit occluders, batched occlusion queries, and CUDA computing.
  • Application of the algorithm to large time-varying datasets exceeding main memory capacity.

Main Results:

  • The PTOT structure provides output-sensitive and asymptotically optimal searching for time-varying data.
  • Queries over consecutive time steps incur no additional searching overhead beyond reporting new active cells.
  • The out-of-core scheme significantly reduces I/O costs and increases GPU computation concurrency.
  • Efficient processing of datasets up to 192GB with a low memory footprint (<= 870MB).

Conclusions:

  • The PTOT indexing structure offers a significant advancement for handling large time-varying scientific data.
  • The proposed out-of-core GPU-accelerated algorithm enables efficient isosurface extraction and view-dependent filtering for massive datasets.
  • This technique demonstrates high performance and scalability, making complex scientific data more accessible for analysis.