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GPU-accelerated minimum distance and clearance queries.

Adarsh Krishnamurthy1, Sara McMains, Kirk Haller

  • 1Department of Mechanical Engineering, Etcheverry Hall, University of California, Berkeley, CA 94720, USA. adarsh@me.berkeley.edu

IEEE Transactions on Visualization and Computer Graphics
|April 9, 2011
PubMed
Summary
This summary is machine-generated.

We developed faster and more accurate algorithms for distance queries on NURBS surfaces using Graphics Processing Units (GPUs). These methods accelerate CAD operations and improve precision for geometric modeling.

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Area of Science:

  • Computer-Aided Design (CAD)
  • Geometric Modeling
  • High-Performance Computing

Background:

  • Distance queries are crucial in CAD for tasks like collision detection and design analysis.
  • Existing methods for complex NURBS surfaces can be computationally intensive and lack precision.

Purpose of the Study:

  • To present practical algorithms for accelerating distance queries on trimmed NURBS surfaces.
  • To provide a generalized framework for using GPUs as coprocessors in CAD operations.
  • To improve the accuracy and speed of geometric computations.

Main Methods:

  • Developed algorithms utilizing programmable Graphics Processing Units (GPUs) for distance queries.
  • Implemented a surface bounding-box hierarchy on the GPU to supplement surface data.
  • Focused on model space precision for accurate results, unlike image space precision methods.

Main Results:

  • Achieved significant acceleration for distance queries on NURBS models.
  • Demonstrated at least an order of magnitude speed increase compared to commercial kernels.
  • Obtained approximately two orders of magnitude improvement in accuracy over ACIS.

Conclusions:

  • The proposed GPU-accelerated algorithms offer a practical and efficient solution for distance queries on NURBS surfaces.
  • The framework generalizes GPU usage for various CAD operations.
  • The methods provide accurate and fast solutions, outperforming existing commercial software.