Browsing by Author "Ament, Marco"

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Open Access
Computational visualization of scalar fields
(2014) Ament, Marco; Weiskopf, Daniel (Prof. Dr.)
Scalar fields play a fundamental role in many scientific disciplines and applications. The increasing computational power offers scientists and digital artists novel opportunities for complex simulations, measurements, and models that generate large amounts of data. In technical domains, it is important to understand the phenomena behind the data to advance research and development in the application domain. Visualization is an essential interface between the usually abstract numerical data and human operators who want to gain insight. In contrast, in visual media, scalar fields often describe complex materials and their realistic appearance is of highest interest by means of accurate rendering models and algorithms. Depending on the application focus, the different requirements on a visualization or rendering must be considered in the development of novel techniques. The first part of this thesis presents three novel optical models that account for the different goals of photorealistic rendering and scientific visualization of volumetric data. In the first case, an accurate description of light transport in the real world is essential for realistic image synthesis of natural phenomena. In particular, physically based rendering aims to produce predictive results for real material parameters. This thesis presents a physically based light transport equation for inhomogeneous participating media that exhibit a spatially varying index of refraction. In addition, an extended photon mapping algorithm is introduced that provides a solution of this optical model. In scientific volume visualization, spatial perception and interactive controllability of the visual representation are usually more important than physical accuracy, which offers researchers more flexibility in developing goal-oriented optical models. This thesis presents a novel illumination model that approximates multiple scattering of light in a finite spherical region to achieve advanced lighting effects like soft shadows and translucency. The main benefit of this contribution is an improved perception of volumetric features with full interactivity of all relevant parameters. Additionally, a novel model for mapping opacity to isosurfaces that have a small but finite extent is presented. Compared to physically based opacity, the presented approach offers improved control over occlusion and visibility of such interval volumes. In addition to the visual representation, the continuously growing data set sizes pose challenges with respect to performance and data scalability. In particular, fast graphics processing units (GPUs) play a central role for current and future developments in distributed rendering and computing. For volume visualization, this thesis presents a parallel algorithm that dynamically decomposes image space and distributes work load evenly among the nodes of a multi-GPU cluster. The presented technique facilitates illumination with volumetric shadows and achieves data scalability with respect to the combined GPU memory in the cluster domain. Distributed multi-GPU clusters become also increasingly important for solving compute-intense numerical problems. The second part of this thesis presents two novel algorithms for efficiently solving large systems of linear equations in multi-GPU environments. Depending on the driving application, linear systems exhibit different properties with respect to the solution set and choice of algorithm. Moreover, the special hardware characteristics of GPUs in combination with the rather slow data transfer rate over a network pose additional challenges for developing efficient methods. This thesis presents an algorithm, based on compressed sensing, for solving underdetermined linear systems for the volumetric reconstruction of astronomical nebulae from telescope images. The technique exploits the approximate symmetry of many nebulae combined with regularization and additional constraints to define a linear system that is solved with iterative forward and backward projections on a distributed GPU cluster. In this way, data scalability is achieved by combining the GPU memory of the entire cluster, which allows one to automatically reconstruct high-resolution models in reasonable time. Despite their high computational power, the fine grained parallelism of modern GPUs is problematic for certain types of numerical linear solvers. The conjugate gradient algorithm for symmetric and positive definite linear systems is one the most widely used solvers. Typically, the method is used in conjunction with preconditioning to accelerate convergence. However, traditional preconditioners are not suitable for efficient GPU processing. Therefore, a novel approach is introduced, specifically designed for the discrete Poisson equation, which plays a fundamental role in many applications. The presented approach builds on a sparse approximate inverse of the matrix to exploit the strengths of the GPU.