M.Sc. thesis defense: Fourier Volume Rendering Of Irregular Data Sets
Presenter: Paul Stark
Examining irregularly sampled data sets usually requires gridding that data set. However, examination of a data set at one particular resolution may not be adequate since either ï¬ne details will be lost, or coarse details will be obscured. In either case, the original data set has been lost. We present an algorithm to create a regularly sampled data set from an irregular one. This new data set is not only an approximation to the original, but allows the original points to be accurately recovered, while still remaining relatively small. This result is accompanied by an effcient zooming operation that allows the user to increase the resolution while gaining new details, all without re-gridding the data. The technique is presented in N -dimensions, but is particularly well suited to Fourier Volume Rendering, which is the fastest known method of direct volume rendering. Together, these techniques allow accurate and effcient, multi-resolution exploration of volume data.