High-quality particle-based volume rendering for large-scale unstructured volume datasets

Abstract

In this article, we propose a technique for improving the image quality of particle-based volume rendering (PBVR). A large-scale unstructured volume dataset often contains multiple sub-volumes, which cannot be ordered by visibility. PBVR can handle this type of volume dataset. Sampling misses often occur when the transfer function undergoes drastic changes, which can result in poor image quality. To reduce sampling misses caused by the high-frequency transfer function, we develop a new sampling technique called “layered sampling”. To confirm the effectiveness of our technique, we apply the proposed technique to a large-scale unstructured volume dataset subdivided into multiple sub-volumes.