  Exascale computing promises quantities of data too large to efficiently store
and transfer across networks in order to be able to analyze and visualize the
results. We investigate Compressive Sensing (CS) as a way to reduce the size of
the data as it is being stored. CS works by sampling the data on the
computational cluster within an alternative function space such as wavelet
bases, and then reconstructing back to the original space on visualization
platforms. While much work has gone into exploring CS on structured data sets,
such as image data, we investigate its usefulness for point clouds such as
unstructured mesh datasets found in many finite element simulations. We sample
using second generation wavelets (SGW) and reconstruct using the Stagewise
Orthogonal Matching Pursuit (StOMP) algorithm. We analyze the compression
ratios achievable and quality of reconstructed results at each compression
rate. We are able to achieve compression ratios between 10 and 30 on moderate
size datasets with minimal visual deterioration as a result of the lossy
compression.
