Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 9-13, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-9-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
19 Aug 2015
AUTOMATIC MODELLING OF RUBBLE MOUND BREAKWATERS FROM LIDAR DATA
M. Bueno, L. Díaz-Vilariño, H. González-Jorge, J. Martínez-Sánchez, and P. Arias Applied Geotechnologies Group, Dept. Natural Resources and Environmental Engineering, University of Vigo, Campus Lagoas-Marcosende, CP 36310 Vigo, Spain
Keywords: breakwaters, water defences, LiDAR, point cloud, 3D modelling Abstract. Rubble mound breakwaters maintenance is critical to the protection of beaches and ports. LiDAR systems provide accurate point clouds from the emerged part of the structure that can be modelled to make it more useful and easy to handle. This work introduces a methodology for the automatic modelling of breakwaters with armour units of cube shape. The algorithm is divided in three main steps: normal vector computation, plane segmentation, and cube reconstruction. Plane segmentation uses the normal orientation of the points and the edge length of the cube. Cube reconstruction uses the intersection of three perpendicular planes and the edge length. Three point clouds cropped from the main point cloud of the structure are used for the tests. The number of cubes detected is around 56 % for two of the point clouds and 32 % for the third one over the total physical cubes. Accuracy assessment is done by comparison with manually drawn cubes calculating the differences between the vertexes. It ranges between 6.4 cm and 15 cm. Computing time ranges between 578.5 s and 8018.2 s. The computing time increases with the number of cubes and the requirements of collision detection.
Conference paper (PDF, 982 KB)


Citation: Bueno, M., Díaz-Vilariño, L., González-Jorge, H., Martínez-Sánchez, J., and Arias, P.: AUTOMATIC MODELLING OF RUBBLE MOUND BREAKWATERS FROM LIDAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 9-13, https://doi.org/10.5194/isprsarchives-XL-3-W3-9-2015, 2015.

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