Computational Geometry

1997 ◽  
pp. 1-17 ◽  
Author(s):  
Mark de Berg ◽  
Marc van Kreveld ◽  
Mark Overmars ◽  
Otfried Schwarzkopf
Keyword(s):  
Computational Geometry

Computational Geometry Column 24

1994 ◽  
Vol 25 (4) ◽  
pp. 12-14
Author(s):  
Joseph O'Rourke
Keyword(s):  
Computational Geometry

Computational Geometry Column 64

2016 ◽  
Vol 47 (4) ◽  
pp. 44-47
Author(s):  
Adrian Dumitrescu
Keyword(s):  
Computational Geometry

The Computational Geometry Column #4

1988 ◽  
Vol 22 (2) ◽  
pp. 111-112 ◽  
Author(s):  
Joseph O'Rourke
Keyword(s):  
Computational Geometry

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

Computational Geometry Column 69

2019 ◽  
Vol 50 (3) ◽  
pp. 75-90 ◽  
Author(s):  
Adrian Dumitrescu ◽  
Minghui Jiang
Keyword(s):  
Computational Geometry
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