View-Dependent Visibility Optimization for Monte Carlo Volume Visualization

Abstract

Compared to classic ray marching-based approaches, Monte Carlo ray tracing for volume visualization can provide faster frame times through progressive rendering, improved image quality, and allows for advanced illumination models more easily. Techniques such as the view-dependent optimization of visibility and illumination of important regions, however, have been formulated for ray marching and rely on stepwise sampling along rays, and are thus incompatible with free-flight distance sampling of state-of-the-art Monte Carlo methods. In this paper we derive such a view-dependent optimization for Monte Carlo ray tracing where the visibility to the camera, the illumination and opacity of important regions is optimized for both single and multiple scattering rendering. For this we define a post-interpolative importance function, introduce an efficient data structure to sample, approximate and optimize the integrated extinction along rays, and devise an efficient Monte Carlo estimator for interactive visualization. Our method enables view-dependent visibility optimization with moderate memory overhead and unbiased, progressive Monte Carlo volume visualization. We demonstrate our method for various volume data sets as well as for data-dependent and spatially-dependent importance functions.