Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka · 2009
We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables `importance sampling' of local regions of interest in the visualization by generating sample points intensively in regions where a user-specified transfer function takes the peak values. The generated sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the generated sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.