Towards Data Centric Sampling for Volume Rendering

We present a new method for sampling the volume rendering integral in volume raycasting where samples are correlated based on transfer function content and data set values. This has two major advantages. First, visual artifacts stemming from structured noise, such as wood grain, can be reduced. Second, we will show that the volume data does no longer need to be available during the rendering phase; a surface representation is used instead, which opens up ample opportunities for rendering of large data. We will show that the proposed sampling method gives higher quality renderings with fewer samples when compared to regular sampling in the spatial