Interactive out-of-core isosurface extraction

Chiang,; Silva, S. Ravi P.; Schroeder,

doi:10.1109/visual.1998.745299

Cited by 86 publications

(85 citation statements)

References 20 publications

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“…Gallagher [29] uses bucketing and linked lists, Livnat et al [42] use k-d trees [7], van Kreveld [61] and Cignoni et al [16] use the interval tree for two-and threedimensional data respectively. Chiang et al [14] use a variant of the interval tree that enables out-of-core isocontour extraction, and use the algorithm of Section 1.2.2 to extend this work to time-varying data [13]. Unlike the spatial techniques that use the octree, which requires regularly gridded data, span space techniques have the advantage of being also applicable to irregularly sampled data.…”

Section: Span-space Techniquesmentioning

confidence: 99%

Isocontour based Visualization of Time-varying Scalar Fields

Mascarenhas

Snoeyink

2009

Mathematics and Visualization

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Summary. Time-varying scalar fields are produced by measurements or simulation of physical processes over time, and must be interpreted with the assistance of computational tools. A useful tool in interpreting the data is graphical visualization, often through level sets, or isocontours of a continuous function derived from the data. In this paper we survey isocontour based visualization techniques for time-varying scalar fields. We focus on techniques that aid selection of meaningful isocontours, and algorithms to extract chosen isocontours. IntroductionPhysical processes that are measured over time, or that are modeled and simulated on a computer, can produce large amounts of time-varying data that must be interpreted with the assistance of computational tools. Such data arises in a wide variety of studies including computational fluid dynamics [15], oceanography [6], medical imaging [60], and climate modeling [46]. The data typically consists of finitely many points in space-time and measured or computed values for each point. Often the values are scalar, with perhaps several scalar values for each sample point. E.g: Pressure, temperature, density. A study of motion or velocity of some kind will result in vector-valued data. In this paper, we focus on scalar-valued data, often called scalar fields. Irrespective of how the points are sampled, we can connect them into a mesh and interpolate the values to obtain a continuous function over the entire domain. Piecewise-linear interpolation is common for large amounts of data, because of its relative ease; multilinear interpolation is also used for regular grids.The goal is to understand the data, usually by exploring it for important features. A medical researcher might be interested in tumors, while a climatologist might be interested in regions of high pressure. Because humans possess a highly developed visual system, transforming the data into images and movies that can be displayed, and providing the scientist with tools to control them can be a powerful visualization method [47]. Popular techniques employed to create such visualizations are direct volume rendering, slicing, and isocontouring. Direct volume rendering employs two classes of algorithms to display all the data: imagespace projection and volume-space projection. In image-space projection algorithms we cast

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Section: Span-space Techniquesmentioning

confidence: 99%

Isocontour based Visualization of Time-varying Scalar Fields

Mascarenhas

Snoeyink

2009

Mathematics and Visualization

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“…Many other techniques use chunks as a unit of storage for data [5] or as a unit of communication between a server that contains the data and clients that visualize the data [6]. A technique similar with chunking but applied to irregular grids is the meta-cell technique [7,8,9]. Meta-cells contain several spatially close cells, have fixed size (several disk blocks) and are loaded from disk as a unit which enables them to be stored in a space saving format.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Chunking for Out-of-Core Volume Visualization Applications

Lipsa

Sparr

Laramee

2009

Advances in Visual Computing

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Abstract. Given the size of today's data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patterns used in volume visualization applications. We propose optimizations that take advantage of extra knowledge about how data is accessed or knowledge about the behavior of previous iterations and can significantly improve performance. We present experimental results that show that dynamic chunking has performance close to regular chunking but has the added advantage that no reorganization of data is required. Dynamic chunking with the proposed optimizations can be significantly faster on average than chunking for certain common data access patterns.

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Section: Introductionmentioning

confidence: 99%

“…Similar to the out-of-core isosurface extraction technique [9], we build an indexing data structure, namely the binary-blocked I/O interval tree (BBIO tree), which is a B-tree-like interval tree and is entirely kept in disk, to index the meta-cells, so that given an isovalue the active meta-cells that are intersected by the isosurface can be located in an I/O-optimal way. In addition, similar to the out-of-core ZSweep algorithm [17], we build a bounding-box file, which is also kept in disk and contains the bounding box and other auxiliary information for each meta-cell, to facilitate the (in-core) ZSweep algorithm [16] (which is based on sweeping a plane in the © -direction) in an I/Oefficient way.…”

Section: Introductionmentioning

confidence: 99%

“…We parallelize the out-of-core isosurface extraction algorithm of [9] and the out-of-core ZSweep technique [17] for direct volume rendering, using the meta-cell technique as a unified underlying building block.Our one-time preprocessing first partitions the dataset into metacells that are stored in disk. From the meta-cells, we build a BBIO tree in disk, which can be used to speed up isosurface extraction, and a bounding-box file in disk, which is used for direct volume rendering.…”

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confidence: 99%

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A unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of unstructured grids

Chiang¹,

Farias²,

Silva³

et al.

Proceedings IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics (Cat. No.01EX520)

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In this paper, we present a unified infrastructure for parallel out-ofcore isosurface extraction and volume rendering of large unstructured grids on distributed-memory parallel machines. We parallelize the out-of-core isosurface extraction algorithm of [9] and the out-of-core ZSweep technique [17] for direct volume rendering, using the meta-cell technique as a unified underlying building block.Our one-time preprocessing first partitions the dataset into metacells that are stored in disk. From the meta-cells, we build a BBIO tree in disk, which can be used to speed up isosurface extraction, and a bounding-box file in disk, which is used for direct volume rendering. At run-time, we use a simple self-scheduling scheme [39] to achieve load balancing among the processors.We perform several experiments on a sixteen-node cluster of PCs connected by a gigabit ethernet, using datasets as large as 6.6 million cells. For the larger datasets, we have found that both our isosurface extraction and direct volume rendering approaches are perfectly scalable up to sixteen nodes.

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Interactive out-of-core isosurface extraction

Cited by 86 publications

References 20 publications

Isocontour based Visualization of Time-varying Scalar Fields

Isocontour based Visualization of Time-varying Scalar Fields

Dynamic Chunking for Out-of-Core Volume Visualization Applications

A unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of unstructured grids

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