Parallel line integral convolution

Zöckler, Malte; Stalling, Detlev; Hege, Hans‐Christian

doi:10.1016/s0167-8191(97)00039-2

Cited by 26 publications

(16 citation statements)

References 2 publications

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“…For that reason, following researches tried to improve the performance of the original LIC algorithm ( [7], [8], [9], [10]). Later, van Wijk [11] proposed a different technique for two-dimensional flow visualization, named Image Based Flow Visualization (IBFV), which presented much better performance if compared to the traditional LIC.…”

Section: Related Workmentioning

confidence: 99%

Visualizing 3D Flow of Black-Oil Reservoir Models on Arbitrary Surfaces Using Projected 2D Line Integral Convolution

Toledo

Celes

2011

2011 24th SIBGRAPI Conference on Graphics, Patterns and Images

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a) Flow on a layer surface (b) Flow near a producer well Fig. 1: Visualization of a 3D oil flow of a black-oil reservoir model.Abstract-In the oil industry, clear and unambiguous 3D flow visualization techniques are very important to inspect the results of numerical simulation of black-oil reservoir models. In this paper, we revisit the use of line integral convolution (LIC) for imaging 3D vector fields on arbitrary surfaces and apply it to reservoir visualization. We use a GPU-based method to image the tangential component using the conventional 2D LIC in projected screen space and propose the use of color to encode the normal component. To attach the flow imaging to the 3D surfaces, avoiding image flickering while manipulating the model, we propose a simple scheme based on randomly generated texture coordinate, avoiding the use of a solid 3D texture noise. For animation, we adjust the use of filters to ensure that the animation speed varies in accordance to the field magnitude. We also explore the visualization of multiphase (oil, gas, and water) flow.

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Section: Related Workmentioning

confidence: 99%

Visualizing 3D Flow of Black-Oil Reservoir Models on Arbitrary Surfaces Using Projected 2D Line Integral Convolution

Toledo

Celes

2011

2011 24th SIBGRAPI Conference on Graphics, Patterns and Images

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“…Early examples include the use of multiprocessor workstations, such as Cray C90, Convex C3240, and SGI systems, to parallelize particle tracing [11,12]. There are also a few research efforts focusing on parallel line integral convolution (LIC) [4,30]. More recently, Muraki et al [17] presented a scalable PC cluster system for enabling simultaneous volume computation and visualization, which includes 3D time-varying LIC volumes for animation.…”

Section: Background and Related Workmentioning

confidence: 99%

Parallel hierarchical visualization of large time-varying 3D vector fields

Wang

2007

Proceedings of the 2007 ACM/IEEE Conference on Supercomputing

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We present the design of a scalable parallel pathline construction method for visualizing large time-varying 3D vector fields. A 4D (i.e., time and the 3D spatial domain) representation of the vector field is introduced to make a timeaccurate depiction of the flow field. This representation also allows us to obtain pathlines through streamline tracing in the 4D space. Furthermore, a hierarchical representation of the 4D vector field, constructed by clustering the 4D field, makes possible interactive visualization of the flow field at different levels of abstraction. Based on this hierarchical representation, a data partitioning scheme is designed to achieve high parallel efficiency. We demonstrate the performance of parallel pathline visualization using data sets obtained from terascale flow simulations. This new capability will enable scientists to study their time-varying vector fields at the resolution and interactivity previously unavailable to them.

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“…Several seed point selection techniques have been proposed, especially for the computation of LIC images, such as selecting points in the scanline order, dividing images into blocks or using Sobol quasi random sequences [12]. We use the following algorithm.…”

Section: Seed Point Selectionmentioning

confidence: 99%

The motion map: efficient computation of steady flow animations

Jobard¹,

Lefer²

Proceedings. Visualization '97 (Cat. No. 97CB36155)

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This paper presents a new approach for animating 2D steady flow fields. It is based on an original data structure called the Motion Map. The Motion Map contains not only a dense representation of the flow field but also all the motion information required to animate the flow. An important feature of this method is that it allows, in a natural way, cyclical variable-speed animations. As far as efficiency is concerned, the advantage of this method is that computing the Motion Map does not take more time than computing a single still image of the flow and the Motion Map has to be computed only once. Another advantage is that the memory requirements for a cyclical animation of an arbitrary number of frames amounts to the memory cost of a single still image.

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Parallel line integral convolution

Cited by 26 publications

References 2 publications

Visualizing 3D Flow of Black-Oil Reservoir Models on Arbitrary Surfaces Using Projected 2D Line Integral Convolution

Visualizing 3D Flow of Black-Oil Reservoir Models on Arbitrary Surfaces Using Projected 2D Line Integral Convolution

Parallel hierarchical visualization of large time-varying 3D vector fields

The motion map: efficient computation of steady flow animations

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