text: Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential equations are solved to most efficiently represent the solution. Particularly in astrophysics and cosmology such simulations now can capture spatial scales ten orders of magnitude apart and more. The irregular locations and extensions of the refined regions in the SAMR scheme and the fact that different resolution levels partially overlap, poses a challenge for GPU-based direct volume rendering methods. kD-trees have proven to be advantageous to subdivide the data domain into non-overlapping blocks of equally sized cells, optimal for the texture units of current graphics hardware, but previous GPU-supported raycasting approaches for SAMR data using this data structure required a separate rendering pass for each node, preventing the application of many advanced lighting schemes that require simultaneous access to more than one block of cells. In this paper we present the first single-pass GPU-raycasting algorithm for SAMR data that is based on a kD-tree. The tree is efficiently encoded by a set of 3D-textures, which allows to adaptively sample complete rays entirely on the GPU without any CPU interaction. We discuss two different data storage strategies to access the grid data on the GPU and apply them to several datasets to prove the benefits of the proposed method.
  @cite_22: We present a framework for interactively visualizing volumetric Adaptive Mesh Renement (AMR) data. For this purpose we employ complex data structures to map the entire AMR dataset to graphics memory. This allows to apply hardware accelerated visualization algorithms previously only operating on uniform cartesian grids. For mapping the data to graphics memory we consider two approaches, a space-efcient, texture-based octree and a fast method based on page table address translation. We demonstrate the utility of our approach by extending an existing GPU raycasting implementation to render AMR data. As a r st step to vector eld visualization techniques we successfully integrated our framework into a commercial CFD postprocessing tool and visualized scalar properties of the vector eld.
  @cite_8: Direct Volume Rendering (DVR) is a technique for creating images directly from a representation of a function defined over a three-dimensional domain. The technique has many application fields, such as scientific visualization and medical imaging. A striking property of the data sets produced within these fields is their ever increasing size and complexity. Despite the advancements of computing resources these data sets seem to grow at even faster rates causing severe bottlenecks in terms of data transfer bandwidths, memory capacity and processing requirements in the rendering pipeline. This thesis focuses on efficient methods for DVR of large data sets. At the core of the work lies a level-of-detail scheme that reduces the amount of data to process and handle, while optimizing the level-of-detail selection so that high visual quality is maintained. A set of techniques for domain knowledge encoding which significantly improves assessment and prediction of visual significance for blocks in a volume are introduced. A complete pipeline for DVR is presented that uses the data reduction achieved by the level-of-detail selection to minimize the data requirements in all stages. This leads to reduction of disk I O as well as host and graphics memory. The data reduction is also exploited to improve the rendering performance in graphics hardware, employing adaptive sampling both within the volume and within the rendered image. The developed techniques have been applied in particular to medical visualization of large data sets on commodity desktop computers using consumer graphics processors. The specific application of virtual autopsies has received much interest, and several developed data classification schemes and rendering techniques have been motivated by this application. The results are, however, general and applicable in many fields and significant performance and quality improvements over previous techniques are shown.
  @cite_1: This paper presents a pipeline for high quality volume rendering of adaptive mesh refinement (AMR) datasets. We introduce a new method allowing high quality visualization of hexahedral cells in this context; this method avoids artifacts like discontinuities in the isosurfaces. To achieve this, we choose the number and placement of sampling points over the cast rays according to the analytical properties of the reconstructed signal inside each cell. We extend our method to handle volume shading of such cells. We propose an interpolation scheme that guarantees continuity between adjacent cells of different AMR levels. We introduce an efficient hybrid CPU-GPU mesh traversal technique. We present an implementation of our AMR visualization method on current graphics hardware, and show results demonstrating both the quality and performance of our method.
  @cite_19: We propose a new approach to efficiently render large volumetric data sets. The system achieves interactive to real-time rendering performance for several billion voxels. Our solution is based on an adaptive data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. One key element of our method is to guide data production and streaming directly based on information extracted during rendering. Our data structure exploits the fact that in CG scenes, details are often concentrated on the interface between free space and clusters of density and shows that volumetric models might become a valuable alternative as a rendering primitive for real-time applications. In this spirit, we allow a quality performance trade-off and exploit temporal coherence. We also introduce a mipmapping-like process that allows for an increased display rate and better quality through high quality filtering. To further enrich the data set, we create additional details through a variety of procedural methods. We demonstrate our approach in several scenarios, like the exploration of a 3D scan (81923 resolution), of hypertextured meshes (163843 virtual resolution), or of a fractal (theoretically infinite resolution). All examples are rendered on current generation hardware at 20--90 fps and respect the limited GPU memory budget.
  @cite_23: Grid-based computing facilitates access to different resources. But management in a grid-based environment isn't centralized. To use grid resources effectively, researchers need a central access point to manage the resources, provide a visual means to explore the data, and record these explorations for further investigation and dissemination. This article describes such a system that's being developed jointly by the University of California, Davis, and the Lawrence Berkeley National Laboratory (LBNL). The centralized system acts as a portal into grid-enabled visualization systems. Scientists using the portal can focus on the important task of extracting insights from their data through visualization instead of having to worry about process management. Because scientists at LBNL and their collaborators require access to the portal from around the world, the portal's interface is entirely Web-based. Authenticated users only need a standards-compliant Web browser to explore their data from anywhere in the world. The portal provides a Web-based interface not just for exploring but also for encapsulating visualization data. Encapsulating the process lets users reproduce the visualization results for validation or extend those results by continuing data exploration. We discuss the integration of our grid-enabled visualization server, the visualization Web application that performs the visualization session management, and the Web-based interface.
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summary: All previous approaches for single-pass multi-resolution GPU-raycasting were based on regular data structures, such as octrees or other partition strategies using regularly shaped nodes @cite_8 @cite_22 @cite_10 @cite_19 . In principle also AMR data structures can be partitioned in blocks of cells from the same resolution level using octrees. However, the resulting tree is usually inefficient, in particular if higher order interpolation is desired, because of the large number of resulting nodes @cite_16 . In contrast kD-trees allow to minimize the number of nodes by adaptively choosing the position of the spatial subdivision planes and have been successfully applied to CPU-, and GPU-based volume rendering of AMR data @cite_17 @cite_23 @cite_15 @cite_1 . In this paper we present the first single-pass GPU-raycasting approach for AMR data based on a kD-partition of the data domain.