2018
DOI: 10.1111/cgf.13439
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Rendering and Extracting Extremal Features in 3D Fields

Abstract: Figure 1: Direct volume renderings (top row) and meshes (bottom row) show the structure of one synthetic dataset. One program computed all renderings, and another computed the mesh vertices. Between features (columns), the only differences in their source code were functions for computing a Newton step to the feature, and for measuring feature strength. These functions were shared between the two programs, achieving orthogonality between implementing visualization algorithms, and specifying the particular feat… Show more

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Cited by 9 publications
(8 citation statements)
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“…For an introduction to the rendering and extraction of extremal features, we refer to Kindlmann et al. (2018).…”
Section: Feature‐based Methodsmentioning
confidence: 99%
“…For an introduction to the rendering and extraction of extremal features, we refer to Kindlmann et al. (2018).…”
Section: Feature‐based Methodsmentioning
confidence: 99%
“…We address both issues in Sections 4.3 and 4.4. Other approaches to ridge extraction attempt to solve the numerical noise problem, including the filtered AMR ridge extraction by Sadlo et al [SP07], which is based on the determination of the gradient by least squares and also works for unstructured grids, and the feature extraction method by Kindlmann et al [KCH∗18], implemented in Diderot [KCS∗16], which solves the numerical noise issue by using ray casting and advanced interpolation schemes.…”
Section: Fundamentalsmentioning
confidence: 99%
“…This list does not fairly describe the sophisticated approaches to high-performance computing [23] and computational scheduling [26,33]. We build on Diderot, a visualization DSL limited to regular grids [8,19,20], but distinguished by offering the mathematical abstraction of a C k tensor field. Our current work extends how Diderot fields are defined to include FEM, so that existing Diderot programs can be used with minimal changes, while introducing a new abstraction, a mesh position, which supports the convenient expression of previous methods of moving through the geometry of a curved FEM mesh [11,25].…”
Section: Related Workmentioning
confidence: 99%
“…To demonstrate point movement within a mesh as a programmable and orthogonal aspect of a FEM visualization algorithm, we augment an existing scientific visualization DSL with a new position type, overloaded operators on positions, and the ability to input FEM data. We chose the Diderot language because it already simplifies implementing streamlines and particle systems on regular grids [19,20], and because its consistent use of a field abstraction facilitates introducing FEM solutions as a new underlying data form.…”
Section: Fem Data Position Types and Overloadingmentioning
confidence: 99%
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