Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Volumetric effects are some of the most challenging effects to render in the field of computer generated images. Depending on the data to generate images from, the challenge lies either in the time budget, or the memory resources available during rendering. Long rendering times often occur when a physically based approach is taken in presence of participating media. Since the goal of physically based rendering is to generate images that are as close to a photo as possible, the rendering process needs to take into account finest details. Rendering large volume data sets, on the other hand, is more severely affected by memory shortage, as these data sets are often present in case of data visualization of three or four dimensional medical or, more general, scientific data. Fortunately, in many cases not all details are important for the viewer and can therefore be omitted, while other details might need to be emphasized to attain an effective visualization. This dissertation aims to address both of these challenges encountered when generating images capturing volumetric effects. It presents several novel approaches to physically based rendering that improve image quality in comparison to other state of the art techniques in that field, while keeping the impact on rendering times low. These approaches include a deep learning technique based on point cloud data, as well as several classic rasterization methods that have a special focus on the translucency effect. The dissertation also includes an evaluation that compares the visual impact of different methods of memory reduction techniques for volume data.