Most CPU-based volume raycasting approaches achieve high performance by advanced memory layouts, space subdivision, and excessive pre-computing. Such approaches typically need an enormous amount of memory. They are limited to sizes which do not satisfy the medical data used in daily clinical routine. We present a new volume raycasting approach based on image-ordered raycasting with object-ordered processing, which is able to perform highquality rendering of very large medical data in real-time on commodity computers. For large medical data such as computed tomographic (CT) angiography run-offs (512x512x1202) we achieve rendering times up to 2.5 fps on a commodity notebook. We achieve this by introducing a memory efficient acceleration technique for on-the-fly gradient estimation and a memory efficient hybrid removal and skipping technique of transparent regions. We employ quantized binary histograms, granular resolution octrees, and a cell invisibility cache. These acceleration structures require just a small extra storage of approximately 10%.

The size of volumetric datasets used in medical environments is increasing at a rapid pace. Due to excessive pre-computation and memory demanding data structures, most current approaches for volume visualization do not meet the requirements of daily clinical routine. In this diploma thesis, an approach for interactive high-quality rendering of large medical data is presented. It is based on image-order raycasting with object-order data traversal, using an optimized cache coherent memory layout. New techniques and parallelization strategies for direct volume rendering of large data on commodity hardware are presented. By using new memory efficient acceleration data structures, high-quality direct volume rendering of several hundred megabyte sized datasets at sub-second frame rates on a commodity notebook is achieved.

This paper presents a Volumetric-CSG (VCSG) method for the representation of volumetric objects and their operation, such as transformations, cutting and Boolean operations. A new volume rendering algorithm is developed for visualizing the VCSG models. The algorithm first generates optimal target blocks for efficient model operations by adaptive subdivision of the target volume, and then volume renders the target blocks using a template-based octree projection process. Both the raycasting block projection and hardware assisted 3D texture mapping rendering methods are implemented.

In this paper we present an algorithm that accelerates 3D texturebased volume rendering of large and sparse data sets. A hierarchical data structure (known as AMR tree) consisting of nested uniform grids is employed in order to efficiently encode regions of interest. The hierarchies resulting from this kind of space partitioning yield a good balance between the amount of volume to render and the number of texture bricks – a prerequisite for fast rendering. Comparing our approach to an octree based algorithm we show that our algorithm increases rendering performance significantly for sparse data. A further advantage is that less parameter tuning is necessary.

Atmospheric simulation is an important means of understanding the environment around us. Through the collection of large amounts of atmospheric data and computer modeling one can predict how variuos particulates such as dirt, smog, and fire can affect our cities and overall public health. However, gleaning insight from numerical