A near optimal isosurface extraction algorithm using the span space

Livnat, Yarden; Shen, Han‐Wei; Johnson, Chris R.

doi:10.1109/2945.489388

Cited by 202 publications

(134 citation statements)

References 10 publications

Supporting

Mentioning

131

Contrasting

Order By: Relevance

“…Livnat et al [42] define the span space as the two dimensional space spanned by the minimum and maximum values of the cells of the volume. A cell c with minimum value minc and maximum value maxc maps to a point (minc, maxc) in the span space.…”

Section: Span-space Techniquesmentioning

confidence: 99%

“…A variety of search structures on the span space have been used to speed-up finding cells that intersect an isocontour. 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].…”

Section: Span-space Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Isocontour based Visualization of Time-varying Scalar Fields

Mascarenhas

Snoeyink

2009

Mathematics and Visualization

View full text Add to dashboard Cite

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

show abstract

Section: Span-space Techniquesmentioning

confidence: 99%

Section: Span-space Techniquesmentioning

confidence: 99%

Isocontour based Visualization of Time-varying Scalar Fields

Mascarenhas

Snoeyink

2009

Mathematics and Visualization

View full text Add to dashboard Cite

show abstract

“…Octrees [9] recursively subdivide space remembering at each stage the interval of values contained in each subdivision. Others important algorithmic acceleration techniques exist [10][11][12] and are very efficient.…”

Section: Previous Workmentioning

confidence: 99%

GPU Accelerated Isosurface Extraction on Tetrahedral Grids

Buatois¹,

Caumon²,

Lévy³

2006

Advances in Visual Computing

View full text Add to dashboard Cite

Abstract. Visualizing large unstructured grids is extremely useful to understand natural and simulated phenomena. However, informative volume visualization is difficult to achieve efficiently due to the huge amount of information to process. In this paper, we present a method to efficiently tessellate on a GPU large unstructured tetrahedral grids made of millions of cells. This method avoids data redundancy by using textures for storing most of the needed data; textures are accessed through vertex texture lookup in the vertex shading unit of modern graphics cards. Results show that our method is about 2 times faster than the same CPUbased extraction, and complementary with previous approaches based on GPU registers: it is less efficient for small grids, but handles millionstetrahedra grids in graphics memory, which was impossible with previous works. Future hardware evolutions are expected to make our approach much more efficient.

show abstract

“…In the algorithm proposed in [28] the cells are mapped into a 2D space, and a kd-tree is created on them, to allow an efficient range search. The tree organizes the points in such a way that, during the traversal, only one among the min and the max values is tested for each level of the tree.…”

Section: Approaches Based On Search Data Structuresmentioning

confidence: 99%

“…The main search data structures adopted are the kd-trees [28] and the interval trees [29]. Domain-based: the domain spanned by the dataset is hierarchically partitioned to disregard the parts of the volume not containing active cells.…”

Section: Approaches Based On Search Data Structuresmentioning

confidence: 99%

Parallel isosurface extraction for 3D data analysis workflows in distributed environments

D’Agostino

Clematis

Gianuzzi

2011

Concurrency and Computation

View full text Add to dashboard Cite

In this paper we discuss the issues related to the development of efficient parallel implementations of the Marching Cubes algorithm, one of the most used methods for isosurface extraction, which is a fundamental operation for 3D data analysis and visualization. We present three possible parallelization strategies and we outline the pros and cons of each of them, considering isosurface extraction as stand-alone operation or as part of a dynamic workflow. Our analysis shows that none of these implementations represents the most efficient solution for arbitrary situations. This is a major issue, because in many cases the quality of the service provided by a workflow depends on the possibility of selecting dynamically the operations to perform and, consequently, the more efficient basic building block for each stage. In this paper we present a set of guidelines that permits to achieve the highest performance for the extraction of isosurface in the most common situations, considering the characteristics of the data to process and of the workflow. These guidelines represent a suitable example to support the efficient configurations of workflows for 3D data processing in a dynamic and complex computing environment

show abstract

A near optimal isosurface extraction algorithm using the span space

Cited by 202 publications

References 10 publications

Isocontour based Visualization of Time-varying Scalar Fields

Isocontour based Visualization of Time-varying Scalar Fields

GPU Accelerated Isosurface Extraction on Tetrahedral Grids

Parallel isosurface extraction for 3D data analysis workflows in distributed environments

Contact Info

Product

Resources

About