|Title||A Data-Dependent Gradient Quantization Scheme for the Acceleration of Volume Rendering
(In Proceedings) |
|in||Visual Data Exploration and Analysis VIII|
Peer Timo Bremer, Oliver Kreylos, Bernd Hamann |
Robert F. Erbacher, Philip C. Chen, Jonathan C. Roberts, Craig M. Wittenbrink, Matti Groehn |
|Publisher||SPIE- The International Society for Optical Engineering|
Volume rendering requires the use of gradient information used as surface normal information, for application of lighting models. However, for interactive applications on-the-fly calculation of gradients is too slow. The common solution to this problem is to quantize gradients of trivariate scalar fields and pre-compute a look-up table prior to the application of a volume rendering method. A number of techniques have been proposed for the quantization of normal vectors, but few have been applied to or adapted for the purpose of volume rendering. We describe an new data-dependent method to quantize gradients using an even number of vectors in a table. The quantization scheme we use is based on a tessellation of the unit sphere. This tessellation represents an "optimally" distributed set of unit normal vectors. Staring with a random tessellation, we optimize the size and distribution of
the tiles (on the unit sphere) with a simulated annealing approach.