Compressive sensing is a technique for efficiently acquiring and reconstructing the data. This technique takes advantage of sparseness or compressibility of the data, allowing the entire measured data to be recovered from relatively few measurements. Considering the fact that the BRDF data often can be highly sparse, we propose to employ the compressive sensing technique for an efficient reconstruction. We demonstrate how to use compressive sensing technique to facilitate a fast procedure for reconstruction of large BRDF data. We have showed that the proposed technique can also be used for the data sets having some missing measurements. Using BRDF measurements of various isotropic materials, we obtained high quality images at very low sampling rates both for diffuse and glossy materials. Similar results also have been obtained for the specular materials at slightly higher sampling rates.

21st International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision -- JUN 24-27, 2013 -- Pilsen, CZECH REPUBLIC

WOS: 000399864900010