Delaunay Mesh Generation

Cheng, Siu-Wing; Dey, Tamal K.; Shewchuk, Jonathan Richard

doi:10.1201/b12987

Cited by 191 publications

(240 citation statements)

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“…The case study indicated that the two algorithms are very close in the quantitative results for area in bridge surface damage detection, which are highly relying on the selection of reference plane as well as the damage criteria. The obvious variances of the results in volume quantification are believed to come from the difference of reference plane generation and the change of volume quantification algorithm using Delaunay's Triangulation (Shewchuk, 1999). …”

Section: Errors In Post Scan Data Analysismentioning

confidence: 99%

Error Sources in Proccessing Lidar Based Bridge Inspection

Bian

Chen

Liu

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Bridge inspection is a critical task in infrastructure management and is facing unprecedented challenges after a series of bridge failures. The prevailing visual inspection was insufficient in providing reliable and quantitative bridge information although a systematic quality management framework was built to ensure visual bridge inspection data quality to minimize errors during the inspection process. The LiDAR based remote sensing is recommended as an effective tool in overcoming some of the disadvantages of visual inspection. In order to evaluate the potential of applying this technology in bridge inspection, some of the error sources in LiDAR based bridge inspection are analysed. The scanning angle variance in field data collection and the different algorithm design in scanning data processing are the found factors that will introduce errors into inspection results. Besides studying the errors sources, advanced considerations should be placed on improving the inspection data quality, and statistical analysis might be employed to evaluate inspection operation process that contains a series of uncertain factors in the future. Overall, the development of a reliable bridge inspection system requires not only the improvement of data processing algorithms, but also systematic considerations to mitigate possible errors in the entire inspection workflow. If LiDAR or some other technology can be accepted as a supplement for visual inspection, the current quality management framework will be modified or redesigned, and this would be as urgent as the refine of inspection techniques.

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Section: Errors In Post Scan Data Analysismentioning

confidence: 99%

Error Sources in Proccessing Lidar Based Bridge Inspection

Bian

Chen

Liu

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Notice that no edges of the triangulation cross the constraints (the boundaries of the polygon). It is known that any polygon (also with holes) can be triangulated without adding extra vertices (de Berg et al 2000;Shewchuk 1997). In our original approach, the triangulation was performed by constructing a CT of all the segments representing the boundaries (exterior and interior) of each polygon.…”

Section: Using a Ct To Repair A Planar Partitionmentioning

confidence: 99%

Solving the horizontal conflation problem with a constrained Delaunay triangulation

Ledoux

Ohori

2016

J Geogr Syst

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Datasets produced by different countries or organisations are seldom properly aligned and contain several discrepancies (e.g., gaps and overlaps). This problem has been so far almost exclusively tackled by snapping vertices based on a user-defined threshold. However, as we argue in this paper, this leads to invalid geometries, is error-prone, and leaves several discrepancies along the boundaries. We propose a novel algorithm to align the boundaries of adjacent datasets. It is based on a constrained Delaunay triangulation to identify and eliminate the discrepancies, and the alignment is performed without moving vertices with a snapping operator. This allows us to guarantee that the datasets have been properly conflated and that the polygons are geometrically valid. We present our algorithm, our implementation (based on the stable and fast triangulator in CGAL), and we show how it can be used it practice with different experiments with real-world datasets. Our experiments demonstrate that our approach is highly efficient and that it yields better results than snapping-based methods.

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“…A malha utilizada no processo de soluçãoé uma malha não estruturada triangular gerada por Triangulação de Delaunay [Edelsbrunner, 2000, Ruppert, 1994, Shewchuk, 1999, Niceno, 2001] e os métodos empregados são: para a equação da pressão será usado o método de volumes finitos e para a equação da energia serão utilizadas diferenças finitas para a derivada temporal e a espacial na direção-z e um esquema de volumes finitos com características semi-Lagrangeanas para os demais termos.…”

Section: Considerações Iniciaisunclassified

“…Uma delas utiliza a triangulação de Delaunay [Shewchuk, 1999, Ruppert, 1994, Edelsbrunner, 2000 [Baliga et al, 1983] e Prakash e Patankar [Prakash e Patankar, 1986].…”

Section: Introductionunclassified

"Simulação do processo de moldagem por injeção 2D usando malhas não estruturadas"

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Delaunay Mesh Generation

Cited by 191 publications

References 0 publications

Error Sources in Proccessing Lidar Based Bridge Inspection

Error Sources in Proccessing Lidar Based Bridge Inspection

Solving the horizontal conflation problem with a constrained Delaunay triangulation

"Simulação do processo de moldagem por injeção 2D usando malhas não estruturadas"

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