2018
DOI: 10.1111/cgf.13449
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An Adaptive Multi‐Grid Solver for Applications in Computer Graphics

Abstract: A key processing step in numerous computer graphics applications is the solution of a linear system discretized over a spatial domain. Often, the linear system can be represented using an adaptive domain tessellation, either because the solution will only be sampled sparsely, or because the solution is known to be ‘interesting’ (e.g. high frequency) only in localized regions. In this work, we propose an adaptive, finite elements, multi‐grid solver capable of efficiently solving such linear systems. Our solver … Show more

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Cited by 20 publications
(15 citation statements)
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References 35 publications
(72 reference statements)
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“…We used the surface reconstruction method, which fits a mesh on the 3D point cloud density of each plot (the filtered point clouds and not voxel grids) (Attene & Spagnuolo, 2000). We applied the Poisson surface reconstruction method, which fits a mesh on all oriented points (perpendicular vectors to the tangential plane to the surface at that point) (Kazhdan & Hoppe, 2019). Poisson surface reconstruction is the preferred method for TLS data due to its stability and reliability (Berger et al., 2014; Gupta & Shukla, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…We used the surface reconstruction method, which fits a mesh on the 3D point cloud density of each plot (the filtered point clouds and not voxel grids) (Attene & Spagnuolo, 2000). We applied the Poisson surface reconstruction method, which fits a mesh on all oriented points (perpendicular vectors to the tangential plane to the surface at that point) (Kazhdan & Hoppe, 2019). Poisson surface reconstruction is the preferred method for TLS data due to its stability and reliability (Berger et al., 2014; Gupta & Shukla, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…Moreover, linear system solvers are important for image processing as these transform linear equations into discrete ones and are often employed in very specific scenarios like image stitching where its solutions are evaluated near the connections of two images. with this in mind, an adaptive, efficient solver was developed in [102] to help solve random levels of a number of finite elements, in symmetric systems, which allows random dimensions and is able to support integral and pointwise constraints.…”
Section: Isosurface Extractionmentioning
confidence: 99%
“…The dimensionality is allowed by integration and evaluation is separated and performed over the existing dimensions using dimensional windows, neighbor lookups, and template specialization. The constraints are supported by allowing the user to impose the coefficients of functions in relation to the B-Spline levels [102].…”
Section: Isosurface Extractionmentioning
confidence: 99%
“…Conceptually different approaches include the popular Screened Poisson Surface Reconstruction (SPSR) [24][25][26][27], which is probably one of the most commonly used point cloud reconstruction methods. In direct comparison, however, SPSR tends to produce rather smoothed out results, whereas ours generally appear sharper and capture finer details, even at lower resolution.…”
Section: Related Workmentioning
confidence: 99%