planes rendered. Right: Refined to 98 textures with extra planes. We present a system for interactively rendering large, unstructured grids. Our approach is to voxelize the grid into a 3D voxel octree, and then to render the data using hierarchical, 3D texture mapping. This approach leverages the current 3D texture mapping PC hardware for the problem of unstructured grid rendering. We specialize the 3D texture octree to the task of rendering unstructured grids through a novel pad and stencil algorithm, which distinguishes between data and non-data voxels. Both the voxelization and rendering processes efficiently manage large, out-ofcore datasets. The system manages cache usage in main memory and texture memory, as well as bandwidths among disk, main memory, and texture memory. It also manages rendering load to achieve interactivity at all times. It maximizes a quality metric for a desired level of interactivity. It has been applied to a number of large data and produces high quality images at interactive, userselectable frame rates using standard PC hardware. 1

Figure 1: RBF reconstruction of unstructured CFD data. (a) Volume rendering of 1,943,383 tetrahedral shock data set using 2,932 RBF functions. (b) Volume rendering of a 156,642 tetrahedral oil reservoir data set using 222 RBF functions organized in a hierarchy of 49 cells. While interactive visualization of rectilinear gridded volume data sets can now be accomplished using texture mapping hardware on commodity PCs, interactive rendering and exploration of large scattered or unstructured data sets is still a challenging problem. We have developed a new approach that allows the interactive rendering and navigation of procedurally-encoded 3D scalar fields by reconstructing these fields on PC class graphics processing units. Since the radial basis functions (RBFs) we use for encoding can provide a compact representation of volumetric scalar fields, the large grid/mesh traditionally needed for rendering is no longer required and ceases to be a data transfer and computational bottleneck during rendering. Our new approach will interactively render RBF encoded data obtained from arbitrary volume data sets, including both structured volume models and unstructured scattered volume models. This procedural reconstruction of large data sets is flexible, extensible, and can take advantage of the Moore’s Law cubed increase in performance of graphics hardware.

Recently, the classic rendering pipeline in 3D graphics hardware has become flexible by means of programmable geometry engines and rasterization units. This development is primarily driven by the mass market of computer games and entertainment software, whose demand for new special effects and more realistic 3D environments induced a reconsideration of the once static rendering pipeline. Besides the impact on visual scene complexity in computer games, these advances in flexibility provide an enormous potential for new volume rendering algorithms. Thereby, they make yet unseen...

In this paper, we present a multiresolution algorithm which is capable to render multiple transparent isosurfaces under real--time constraints. To this end, the underlying 3D data set is covered with a hierarchical tetrahedral grid.

Datasets of tens of gigabytes are becoming common in computational and experimental science. This development is driven by advances in imaging technology, producing detectors with growing resolutions, as well as availability of cheap processing power and memory capacity in commodity-based computing clusters. In this article we describe the design of a visualization system that allows scientists to interactively explore large remote data sets in an efficient and flexible way. The system is broadly applicable and currently used by medical scientists conducting an osteoporosis research project. Human vertebral bodies are scanned using a high resolution micro-CT scanner producing scans of roughly 8 GB size each. All participating research groups require access to the centrally stored data. Due to the rich internal bone structure, scientists need to interactively explore the full dataset at coarse levels, as well as visualize subvolumes of interest at the highest resolution. Our solution is based on HDF5 and GridFTP. When accessing data remotely, the HDF5 data processing pipeline is modified to support efficient retrieval of subvolumes. We reduce the overall latency and optimize throughput by executing high-level operations on the remote side. The GridFTP protocol is used to pass the HDF5 requests to a customized server. The approach takes full advantage of local graphics hardware for rendering. Interactive visualization is accomplished using a background thread to access the datasets stored in a multi-resolution format. A hierarchical volume renderer provides seamless integration of high resolution details with low resolution overviews.

Abstract—Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information. Index Terms—Focus & Context, GPU rendering, volume raycasting.

We have developed a hierarchical paging scheme for handling very large volumetric data sets at interactive frame rates. Our system trades texture resolution for speed and uses effective prediction strategies. We have tested our approach for datasets with up to 16GB in size and show that it works well with less than 500MB of main memory. Our approach makes it feasible to deal with these volumes on desktop machines.

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Raw data HDR image (pseudo color encoded) Tone mapped image Figure 1: Pipeline of high dynamic range volume visualization. The input is a scalar volume with high precision and/or high resolution (e.g. 2048 3). The user defines a transfer function using a novel non-linear magnification interface. The volume rendering output is in high dynamic range image format. By applying a tone mapping operator, the final result can be displayed on a regular low dynamic range display device. High resolution volumes require high precision compositing to preserve detailed structures. This is even more desirable for volumes with high dynamic range values. After the high precision intermediate image has been computed, simply rounding up pixel values to regular display scales loses the computed details. In this paper, we present a novel high dynamic range volume visualization method for rendering volume data with both high spatial and intensity resolutions. Our method performs high precision volume rendering followed by dynamic tone mapping to preserve details on regular display devices. By leveraging available high dynamic range image

We describe a system for the texture-based direct volume visualization of large data sets on a PC cluster equipped with GPUs. The data is partitioned into volume bricks in object space, and the intermediate images are combined to a final picture in a sort-last approach. Hierarchical wavelet compression is applied to increase the effective size of volumes that can be handled. An adaptive rendering mechanism takes into account the viewing parameters and the properties of the data set to adjust the texture resolution and number of slices. We discuss the specific issues of this adaptive and hierarchical approach in the context of a distributed memory architecture and present corresponding solutions. Furthermore, our compositing scheme takes into account the footprints of volume bricks to minimize the costs for reading from framebuffer, network communication, and blending. A detailed performance analysis is provided for several network, CPU, and GPU architectures--and scaling characteristics of the parallel system are discussed. For example, our tests on a 8-node AMD64 cluster with InfiniBand show a rendering speed of 6 frames per second for a 2048x1024x1878 data set on a 1024^2 viewport.