Scattered data interpolation in three or more variables
Mathematical methods in computer aided geometric design
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Incremental topological flipping works for regular triangulations
SCG '92 Proceedings of the eighth annual symposium on Computational geometry
Comparing methods of interpolation for scattered volumetric data
State of the art in computer graphics
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
REINAS: the Real-time Environmental Information Network and Analysis System
COMPCON '95 Proceedings of the 40th IEEE Computer Society International Conference
Surface reconstruction based on compactly supported radial basis functions
Geometric modeling
Volume rendering data with uncertainty information
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
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Data sets with large number of missing values pose a common problem because most standard scientific visualization algorithms fail when presented with incomplete cells. In this article we discuss the pros, cons, and pitfalls of the alternatives and present our experience in dealing with gridded data sets with missing or invalid scalar data. In our study we emphasized visualization methods that exploit the clustering pattern in the data. We applied our findings to data acquired from Nexrad (next generation radars) weather radars, which usually have no more than 3 to 4 percent of all possible cell points filled.