LOOPS: LOcally Optimized Polygon Simplification

Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed pos...

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Vydáno v:Computer graphics forum Ročník 41; číslo 3; s. 355 - 365
Hlavní autoři: Amiraghdam, Alireza, Diehl, Alexandra, Pajarola, Renato
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.06.2022
Témata:
Algorithms
Apexes
Cartography
CCS Concepts
Computing methodologies → Rendering
Datasets
Human‐centered computing → Geographic visualization
Polygons
Rasterization
Rendering
Simplification
Theory of computation → Computational geometry
Visualization
Visualization techniques
ISSN:0167-7055, 1467-8659
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Shrnutí:Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU‐based level‐of‐detail (LOD) polyline simplification and rendering approach has been presented which can perform locally‐adaptive LOD visualization of large‐scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large‐scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally‐optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point‐inside‐polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas‐Peucker and Wang‐Müller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14546