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GTC ON-DEMAND

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Abstract:

We present new GPU algorithms for computing the directed Hausdorff distance between freeform surfaces, with applications in shape matching, mesh simplification, and geometric approximation and optimization. Our algorithms run in real-time with very small error bounds for parametric models defined by complex NURBS surfaces and can be used to interactively compute the Hausdorff distance for models made of dynamic deformable surfaces. We discuss implementation decisions and tradeoffs between OpenGL, Cuda, and Thrust, and the advantages and disadvantages of parallel hierarchical culling methods for this application.

We present new GPU algorithms for computing the directed Hausdorff distance between freeform surfaces, with applications in shape matching, mesh simplification, and geometric approximation and optimization. Our algorithms run in real-time with very small error bounds for parametric models defined by complex NURBS surfaces and can be used to interactively compute the Hausdorff distance for models made of dynamic deformable surfaces. We discuss implementation decisions and tradeoffs between OpenGL, Cuda, and Thrust, and the advantages and disadvantages of parallel hierarchical culling methods for this application.

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Topics:
Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2410
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Abstract:

Cloud computing for mechanical CAD provides centrally stored and synchronized models for concurrent engineering. For compactness, trimmed parametric NURBS surface representations are optimal for data transfer to client devices, which must evaluate and render models locally. Direct GPU rendering without pre-tessellation is an attractive solution in this context, both for speed and to preserve fidelity to the original geometry. However, existing data-parallel direct rendering approaches for NURBS suffer from rendering artifacts at trim boundaries. This talk proposes a solution to address these rendering artifacts that are still preventing wide-scale adoption of all such direct rendering algorithms for trimmed parametric models.

Cloud computing for mechanical CAD provides centrally stored and synchronized models for concurrent engineering. For compactness, trimmed parametric NURBS surface representations are optimal for data transfer to client devices, which must evaluate and render models locally. Direct GPU rendering without pre-tessellation is an attractive solution in this context, both for speed and to preserve fidelity to the original geometry. However, existing data-parallel direct rendering approaches for NURBS suffer from rendering artifacts at trim boundaries. This talk proposes a solution to address these rendering artifacts that are still preventing wide-scale adoption of all such direct rendering algorithms for trimmed parametric models.

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Topics:
Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2411
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Speakers:
Adarsh Krishnamurthy, Sara McMains
- University of California Berkeley
Abstract:
The broad objective of our research is to develop mechanical Computer-Aided Design tools that provide interactive feedback to the designer. We have developed GPU algorithms for fundamental CAD operations (NURBS evaluation, surface-surface intersection, separation distance computation, moment computation, etc.) that are one to two orders of magnitude faster, and often more accurate, than current commercial CPU implementations. We will touch on strategies we have employed to meet GPU programming challenges, such as the separation of CPU/GPU operations, imposing artificial structure on computations, and transforming problem definitions to suit GPU-computation models.
The broad objective of our research is to develop mechanical Computer-Aided Design tools that provide interactive feedback to the designer. We have developed GPU algorithms for fundamental CAD operations (NURBS evaluation, surface-surface intersection, separation distance computation, moment computation, etc.) that are one to two orders of magnitude faster, and often more accurate, than current commercial CPU implementations. We will touch on strategies we have employed to meet GPU programming challenges, such as the separation of CPU/GPU operations, imposing artificial structure on computations, and transforming problem definitions to suit GPU-computation models.  Back
 
Topics:
Developer - Algorithms, Tools & Libraries, Graphics and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
S102171
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