APRIL 18-21, 2017
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Efficient GPU-Accelerated Computation of Isosurface Similarity Maps
We present an efficient GPU-based solution to compute isosurface \ similarity maps for scientific volume data sets. Our approach first \ replaces exact isosurface extraction with a binary volume indicat- \ ing whether each voxel intersects the surface or not. We then em- \ ploy bounding volume hierarchy (BVH)-trees to speed up the dis- \ tance field computation. Finally, a self-similarity map is gener- \ ated from which we identify representative isosurfaces. We apply \ our approach to compute isosurface similarity maps from different \ volume data sets of varying sizes and characteristics. The results \ demonstrate significant speed gain with acceptable loss of accu- \ racy, showing the potential of our solution for handling large-scale \ time-varying multivariate data sets.