Parallel constrained Delaunay triangulation on the GPU

Coll, Narcís; Guerrieri, Marité

doi:10.1080/13658816.2017.1300804

Cited by 8 publications

(4 citation statements)

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“…The experimental results demonstrate that the percentage of missed triangles in the algorithm's triangulations compared to CGAL is less than 0. In [25], a method is presented for calculating the 2Dconstrained Delaunay triangulation (CDT) of a planar straight-line graph (PSLG) composed of points and segments. This method involves simultaneous insertion of points and segments into the triangulation while carefully handling conflicts that may arise from concurrent insertion of points or edge flips.…”

Section: ) Quasi-delaunay Triangulations Using Gpu-based Edge-flipsmentioning

confidence: 99%

“…In the literature, several algorithms have been developed in order to compute Delaunay triangulations, e.g., [14]- [16]. Moreover, in the last few years, the increasing demand for high-performance computing has led to the implementation of Delaunay triangulation algorithms on different hardware platforms, including CPUs [17]- [21], GPUs [22]- [25], and FPGAs [26]- [31]. These implementations aim to improve the computational efficiency and reduce the running time of triangulation algorithms, especially for large data sets.…”

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confidence: 99%

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A Comprehensive Survey on Delaunay Triangulation: Applications, Algorithms, and Implementations Over CPUs, GPUs, and FPGAs

Elshakhs,

Deliparaschos,

Charalambous

et al. 2024

IEEE Access

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Section: ) Quasi-delaunay Triangulations Using Gpu-based Edge-flipsmentioning

confidence: 99%

mentioning

confidence: 99%

A Comprehensive Survey on Delaunay Triangulation: Applications, Algorithms, and Implementations Over CPUs, GPUs, and FPGAs

Elshakhs,

Deliparaschos,

Charalambous

et al. 2024

IEEE Access

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“…Manuscript to be reviewed Computer Science analysis (Zhang et al, 2017), urban growth simulation (Guan et al, 2016), Delaunay Triangulation (DT) for GIS (Coll and Guerrieri, 2017), spatial interpolation (Wang et al, 2017;Cheng, 2013;Mei, 2014;Mei et al, 2017;Mei, 2014;Ding et al, 2018b), and image processing (Wasza et al, 2011;Lei et al, 2011;Yin et al, 2014;Wu et al, 2018).…”

Section: /29mentioning

confidence: 99%

Peer Review #2 of "Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models (v0.1)"

2020

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The building of large-scale Digital Elevation Models (DEMs) using various interpolation algorithms is one of the key issues in geographic information science. Different choices of interpolation algorithms may trigger significant differences in interpolation accuracy and computational efficiency, and a proper interpolation algorithm needs to be carefully used based on the specific characteristics of the scene of interpolation. In this paper, we comparatively investigate the performance of parallel Radial Basis Function (RBF)-based, Moving Least Square (MLS)-based, and Shepard's interpolation algorithms for building DEMs by evaluating the influence of terrain type, raw data density, and distribution patterns on the interpolation accuracy and computational efficiency. The drawn conclusions may help select a suitable interpolation algorithm in a specific scene to build large-scale DEMs.

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“…• Hardwired application-specific mesh processing implementations (e.g., Delaunay triangulation [Coll and Guerrieri 2017], mesh painting [Schäfer et al 2014], and rendering subdivision surfaces [Tobler and Maierhofer 2006]). Such implementations may achieve best-of-class performance on a particular problem, but their data structures are specific to that problem.…”

Section: Introductionmentioning

confidence: 99%

RXMesh

2021

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We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons , thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure RXMesh : Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications---mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.

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Parallel constrained Delaunay triangulation on the GPU

Cited by 8 publications

References 14 publications

A Comprehensive Survey on Delaunay Triangulation: Applications, Algorithms, and Implementations Over CPUs, GPUs, and FPGAs

A Comprehensive Survey on Delaunay Triangulation: Applications, Algorithms, and Implementations Over CPUs, GPUs, and FPGAs

Peer Review #2 of "Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models (v0.1)"

RXMesh

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