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Related Experiment Video

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Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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RACBVHs: random-accessible compressed bounding volume hierarchies.

Tae-Joon Kim1, Bochang Moon, Duksu Kim

  • 1Korea Advanced Institute of Science and Technology, Daejeon. tjkim@tclab.kaist.ac.kr

IEEE Transactions on Visualization and Computer Graphics
|January 16, 2010
PubMed
Summary

We developed random-accessible compressed bounding volume hierarchies (RACBVHs) for efficient random access to massive models. This novel compressed BVH representation significantly speeds up applications like ray tracing and collision detection.

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Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Data Structures

Background:

  • Bounding Volume Hierarchies (BVHs) are crucial for accelerating geometric queries in massive models.
  • Existing BVH representations often lack efficient random access capabilities, hindering performance for large datasets.
  • Decompressing entire BVHs for random access is computationally expensive and time-consuming.

Purpose of the Study:

  • To introduce a novel compressed BVH representation, Random-Accessible Compressed Bounding Volume Hierarchies (RACBVHs).
  • To enable efficient random access on compressed BVHs without full decompression.
  • To improve runtime performance in applications like ray tracing and collision detection.

Main Methods:

  • Decomposing BVHs into compressed clusters, where each cluster contains consecutive bounding volume nodes.
  • Compressing each cluster independently, serving as an access point for the RACBVH.
  • Developing a general BVH access API and a runtime decompression framework for transparent access.
  • Extending the method to support parallel random access utilizing multicore CPU architectures.

Main Results:

  • Achieved compression ratios of up to 12:1.
  • Demonstrated decompression speeds of 4.2 million BV nodes per second on a single CPU core.
  • Improved runtime performance by over a factor of 4 in ray tracing and collision detection compared to uncompressed data.
  • Reduced data access time through selective fetching and decompression of small compressed BVH regions.

Conclusions:

  • RACBVHs offer a compact and efficient solution for random access on massive BVHs.
  • The proposed method significantly enhances performance in geometric applications by optimizing data access and decompression.
  • RACBVHs are a promising advancement for handling large-scale models in computer graphics and related fields.