Hardware-Assisted Feature Analysis and Visualization of Procedurally Encoded Multifield Volumetric Data

Weiler, Manfred; Botchen, Ralf P.; Stegmaier, Simon; Ertl, Thomas; Huang, Jingshu; Jang, Yun; Ebert, David S.; Gaither, Kelly

doi:10.1109/mcg.2005.106

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…With regard to the latter, there is some research on leveraging RBFs on GPUs in the fields of visualization [13,53], surface reconstruction [7,12], and neural networks [5]. However, research on the parallelization of RBF algorithms to solve PDEs on multiple CPU/GPU architectures is essentially non-existent.…”

Section: Introductionmentioning

confidence: 99%

Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

Bollig

Flyer

Erlebacher

2012

Journal of Computational Physics

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Section: Introductionmentioning

confidence: 99%

Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

Bollig

Flyer

Erlebacher

2012

Journal of Computational Physics

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“…Local fitting with the radial basis functions (Jang et al 2004;Weiler et al 2005) can also be performed to obtain higher accuracy. Exception handling is executed if one of the following conditions is satisfied: (a) the compact support contains fewer than seven voxels, and (b) the maximum width of the compact support is less than the smallest intervoxel distance.…”

Section: Algorithm Of the Volumic Mpumentioning

confidence: 99%

“…There are methods to fit both regular and irregular-grid data using radial basis functions (Jang et al 2004;Weiler et al 2005). However, full application of the radial basis functions for all of the data requires significant memory and computation time.…”

Section: Introductionmentioning

confidence: 99%

Data fitting independent of grid structure using a volumic version of MPU

Tsukamoto

Kawashima

Inoue

et al. 2011

J Vis

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Volume graphics is a key technology in fields such as fluid dynamics and medical science. The visualization of volume data requires the creation of a continuous scalar field to exactly or approximately interpolate scalar values assigned to the discrete voxels. In the present paper, we propose a method that we refer to as the volumic version of the multi-level partition of unity (volumic MPU). The method approximately interpolates the scalar values with good precision to generate a scalar field that is continuous up to second-order differentiation. The volumic MPU, being independent of grid structures of input volume data, is applicable to both irregular-grid data and regular grid data. The volumic MPU can also be used as an effective data-compression technique. The speed to evaluate the created scalar field is almost as high as that of trilinear interpolation.

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“…Not only do they allow us to preserve local features that may be present in our high-dimensional trapping probability surface, they are also robust with respect to outliers which typically occur in stochastic experiments. We have selected Gaussian RBFs for developing our simplified models [16].…”

Section: Selecting Model Simplification Techniquementioning

confidence: 99%

Generating Simplified Trapping Probability Models From Simulation of Optical Tweezers System

Banerjee

Balijepalli

Gupta

et al. 2009

Journal of Computing and Information Science in Engineering

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This paper presents a radial basis function based approach to generate simplified models to estimate the trapping probability in optical trapping experiments using offline simulations. The difference form of Langevin’s equation is used to perform physically accurate simulations of a particle under the influence of a trapping potential and is used to estimate trapping probabilities at discrete points in the parameter space. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models of trapping probability. We show that the proposed approach is computationally efficient in estimating the trapping probability and that the estimated probability using the simplified models is sufficiently close to the probability estimates from offline simulation data.

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Hardware-Assisted Feature Analysis and Visualization of Procedurally Encoded Multifield Volumetric Data

Cited by 25 publications

References 14 publications

Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

Data fitting independent of grid structure using a volumic version of MPU

Generating Simplified Trapping Probability Models From Simulation of Optical Tweezers System

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