Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
A signal processing approach to fair surface design
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
The challenges of visualizing and modeling environmental data
Proceedings of the 7th conference on Visualization '96
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
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
View-dependent multiresolution splatting of non-uniform data
VISSYM '02 Proceedings of the symposium on Data Visualisation 2002
Wind Field Retrieval and Display for Doppler Radar Data
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Revealing uncertainty for information visualization
Information Visualization
Visualizing missing data: graph interpretation user study
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Acquisition and display of real-time atmospheric data on terrain
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
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Much of the research in scientific visualization has focused on complete sets of gridded data. This paper presents our experience dealing with gridded data sets with large number of missing or invalid data, and some of our experiments in addressing the shortcomings of standard off-the-shelf visualization algorithms. In particular, we discuss the options in modifying known algorithms to adjust to the specifics of sparse datasets, and provide a new technique to smooth out the side-effects of the operations. We apply 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.