Reconstruction of 3D laser scanned point clouds may generate a mesh characterized by a high number of triangles. Unfortunately, in Computer Aided Design environments neither a simple triangle reduction, nor decimation filters are feasible for mesh optimization, because of their intrinsic errors. In this paper we show how Genocop III can be effectively used to reconstruct a point cloud bounding the error under a certain threshold. Moreover, we define an optimized algorithm for evaluating the reconstruction error, that exploits AABB-trees and pre-computation and provides a useful metric to the genetic algorithm.
An Evolutionary Optimization Method for Parameter Search in 3D Points Cloud Reconstruction / Bevilacqua, V; Ivona, F; Cafarchia, D; Marino, F (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Theories : 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings / [a cura di] De-Shuang Huang, Vitoantonio Bevilacqua, Juan Carlos Figueroa, Prashan Premaratne. - STAMPA. - Berlin; Heidelberg : Springer, 2013. - ISBN 978-3-642-39478-2. - pp. 601-611 [10.1007/978-3-642-39479-9_70]
An Evolutionary Optimization Method for Parameter Search in 3D Points Cloud Reconstruction
Bevilacqua V;Marino F
2013-01-01
Abstract
Reconstruction of 3D laser scanned point clouds may generate a mesh characterized by a high number of triangles. Unfortunately, in Computer Aided Design environments neither a simple triangle reduction, nor decimation filters are feasible for mesh optimization, because of their intrinsic errors. In this paper we show how Genocop III can be effectively used to reconstruct a point cloud bounding the error under a certain threshold. Moreover, we define an optimized algorithm for evaluating the reconstruction error, that exploits AABB-trees and pre-computation and provides a useful metric to the genetic algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
