Data Level Comparison of Surface Classification and Gradient Filters

Abstract

Surface classification and shading of three dimensional scalar data sets are important enhancements for direct volume rendering (DVR). However, unlike conventional surface rendering, DVR algorithms do not have explicit geometry to shade, making it difficult to perform comparisons. Furthermore, DVR, in general, involves a complex set of parameters whose effects on a rendered image are hard to compare. Previous work uses analytical estimations of the quality of interpolation, gradient filters, and classification. Typical comparisons are done using side-by-side examination of rendered images. However, non-linear processes are involved in the rendering pipeline and thus the comparison becomes particularly difficult. In this paper, we present a data level methodology for analyzing volume surface classification and gradient filters. Users can more effectively estimate algorithmic differences by using intermediate information. Based on this methodology, we also present new data level metrics and examples of analyzing differences in surface classification and gradient calculation. Please refer to www.cse.ucsc.edu/research/avis/dvr.html for a full color version of this paper.