High-quality rendering with depth cueing of volumetric data using Monte Carlo integration

Visualizing efficiently volumetric data still remains a challenge in many fields such as medical imaging and scientific visualization. Monte Carlo volume rendering is a novel and efficient visualization technique for very large datasets. However, when taking into account depth cueing, the volume rendering integral becomes complex, and it is difficult to sample efficiently and in a viewing-independent manner. In this paper, we propose an efficient volume rendering method by dividing the volume rendering integral into four sub-integrals and enabling sampling in each sub-integral to be “best” while achieving viewing independency. As a result, we get a better estimation of the integral than the classical sampling method. The results show that thus rendered images exhibit high quality.