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Issue No.10  Oct. (2012 vol.18)
pp: 16031613
Wesley Griffin , University of Maryland, Baltimore County, Baltimore
Yu Wang , University of Maryland, Baltimore County, Baltimore
David Berrios , National Aeronautics and Space Administration, Greenbelt
Marc Olano , University of Maryland, Baltimore County, Baltimore
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.113
ABSTRACT
Surface curvature is used in a number of areas in computer graphics, including texture synthesis and shape representation, mesh simplification, surface modeling, and nonphotorealistic line drawing. Most realtime applications must estimate curvature on a triangular mesh. This estimation has been limited to CPU algorithms, forcing object geometry to reside in main memory. However, as more computational work is done directly on the GPU, it is increasingly common for object geometry to exist only in GPU memory. Examples include vertex skinned animations and isosurfaces from GPUbased surface reconstruction algorithms. For static models, curvature can be precomputed and CPU algorithms are a reasonable choice. For deforming models where the geometry only resides on the GPU, transferring the deformed mesh back to the CPU limits performance. We introduce a GPU algorithm for estimating curvature in real time on arbitrary triangular meshes. We demonstrate our algorithm with curvaturebased NPR feature lines and a curvaturebased approximation for an ambient occlusion. We show curvature computation on volumetric data sets with a GPU isosurface extraction algorithm and vertexskinned animations. We present a graphics pipeline and CUDA implementation. Our curvature estimation is up to {\sim}18{\times} faster than a multithreaded CPU benchmark.
INDEX TERMS
Graphics processing unit, Isosurfaces, Face recognition, Real time systems, ambient occlusion., Realtime rendering, GPU, geometry shader, curvature, line drawing
CITATION
Wesley Griffin, Yu Wang, David Berrios, Marc Olano, "RealTime GPU Surface Curvature Estimation on Deforming Meshes and Volumetric Data Sets", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 10, pp. 16031613, Oct. 2012, doi:10.1109/TVCG.2012.113
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