Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
A comparison of normal estimation schemes
VIS '97 Proceedings of the 8th conference on Visualization '97
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Opacity-weighted color interpolation, for volume sampling
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Design of accurate and smooth filters for function and derivative reconstruction
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Structured spatial domain image and data comparison metrics
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Frequency Analysis of Gradient Estimators in Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Ray-Based Data Level Comparisons of Direct Volume Rendering Algorithms
Dagstuhl '97, Scientific Visualization
An evaluation of reconstruction filters for volume rendering
VIS '94 Proceedings of the conference on Visualization '94
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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.