### Refine

#### Year of publication

#### Document Type

- In Proceedings (165)
- Article (134)
- ZIB-Report (62)
- Book chapter (16)
- Other (14)
- In Collection (9)
- Book (3)
- Poster (3)

#### Keywords

- volume rendering (3)
- 3D texture mapping (2)
- DVR (2)
- visualization (2)
- 3D neural network (1)
- AMR (1)
- AMR hierarchies (1)
- AMR tree (1)
- Amira (1)
- CCTK (1)

#### Institute

- Visual and Data-centric Computing (358)
- Visual Data Analysis (348)
- Visual Data Analysis in Science and Engineering (242)
- Therapy Planning (76)
- Image Analysis in Biology and Materials Science (66)
- ZIB Allgemein (31)
- Numerical Mathematics (22)
- Vergleichende Visualisierung (12)
- Computational Medicine (5)
- Geometric Data Analysis and Processing (5)

Fast Volume Rendering of Sparse High-Resolution Datasets Using Adaptive Mesh Refinement Hierarchies
(2001)

In this paper we present an algorithm that accelerates 3D texture-based 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.

Many phenomena in nature and engineering happen simultaneously on rather diverse spatial and temporal scales, i.e.\ exhibit a multi-scale character. Therefore various hierarchical data structures and numerical schemes have been devised to represent quantitatively such phenomena. A special numerical multilevel technique, associated with a particular hierarchical data structure, is so-called Adaptive Mesh Refinement (AMR). This scheme achieves locally very high spatial and temporal resolutions. Due to its popularity, many scientists are in need of interactive visualization tools for AMR data. In this article we present a 3D texture-based volume rendering algorithm for AMR data, that directly utilizes the hierarchical structure. Thereby interactive rendering even for large data sets is achieved. In particular the problems of interpolation artifacts, opacity corrections, and texture memory limitations are addressed. The algorithm's value in practice is demonstrated with simulation and image data.

Regional hyperthermia, a clinical cancer therapy, is the main topic of the Sonderforschungsbereich Hyperthermia: Scientific Methods and Clinical Applications'' at Berlin. In recent years, technological improvements towards a better concentration of heat to the desired target region have been achieved. These include a rather sophisticated integrated software environment for therapy planning and a new hyperthermia applicator. In a next step, a detailed closed loop monitoring of the actual treatment is to be developed. For this purpose the hyperthermia applicator is combined with an MRI system, which will allow to check the positioning of the patients and to measure individual blood perfusion as well as the 3D temperature distribution. The measurements will then be employed for an on-line control of the whole treatment. In this intended setting, new fast feedback control algorithms will come into play.

This report contains paper abstracts of the workshop "Visualization and Mathematics" held in Berlin-Dahlem in September 1997. The meeting serves as a forum for an international community of researchers and practitioners on the application of visualization techniques in mathematics and on mathematical concepts in visualization. It is the second symposium in a series of workshops bringing together mathematicians and experts from scientific visualization. The themes of the workshop include: \begin{itemize} \item - applications in differential geometry and partial differential equations \item - algorithmic aspects of adaptive and hierarchical techniques in space and time \item - time control of animated objects and corresponding algorithms \item - algorithmic representation of objects for display, storage and exchange \item - new visualization techniques for mathematical structures \item - integration of visualization with symbolic and numerical computation. \end{itemize}

The increasing demand for distributed solutions in computing technology does not stop when it comes to visualization techniques. However, the capabilities of todays applications to perform remote rendering are limited by historical design legacys. Especially the popular X11 protokoll, which has been proven to be extremely flexible and usefull for remote 2D graphics applications, breaks down for the case of remote 3D rendering. In this white paper, we give a short overview of generic remote rendering technologies available today, and compare their performance to the recently released vizserver by SGI: a network extension to the SGI OpenGL rendering engines.

We propose an approach for transforming the sampling of a molecular conformation distribution into an analytical model based on Hidden Markov Models. The model describes the sampled shape density as a mixture of multivariate unimodal densities. Thus, it delivers an interpretation of the sampled density as a set of typical shapes that appear with different probabilities and are characterized by their geometry, their variability and transition probabilities between the shapes. The gained model is used to identify atom groups of constant shape that are connected by metastable torsion angles. Based on this description an alignment for the original sampling is computed. As it takes into account the different shapes contained in the sampled set, this alignment allows to compute reasonable average shapes and meaningful shape density plots. Furthermore, it enables us to visualize typical conformations.

This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.

The Monte Carlo simulation of the dynamics of complex molecules produces trajectories with a large number of different configurations to sample configuration space. It is expected that these configurations can be classified into a small number of conformations representing essential changes in the shape of the molecule. We present a method to visualize these conformations by point sets in the plane based on a geometrical distance measure between individual configurations. It turns out that different conformations appear as well-separated point sets. The method is further improved by performing a cluster analysis of the data set. The point-cluster representation is used to control a three-dimensional molecule viewer application to show individual configurations and conformational changes. The extraction of essential coordinates and visualization of molecular shape is discussed.

The FORTRAN preprocessor fpp in the newly introduced Autotasking System of CRAY Research allows automatic vectorization and parallelization on basis of a data dependence analysis. An introduction into data dependence analysis is given, showing how data dependence graphs unveil opportunities for program transformations like vectorization and concurrentization. The report contains a complete description of the preprocessors functionality, its options and directives for increasing the effectiveness of the dependence analyzer and steering the code transformations. Finally, some advice is given for the practical use of fpp on CRAY computers.

Efficient implementations of irregular problems on vector and parallel architectures are generally hard to realize. An important class of problems are Gauß-Seidel iteration schemes applied to irregular data sets. The unstructured data dependences arising there prevent restructuring compilers from generating efficient code for vector or parallel machines. It is shown, how to structure the data dependences by decomposing the underlying data set using graph coloring techniques and by specifying a particular execution order already on the algorithm level. Methods to master the irregularities originating from different types of tasks are proposed. An application is given and some open issues and future developments are discussed.