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. © 2013 Springer-Verlag.
CITATION STYLE
Bevilacqua, V., Ivona, F., Cafarchia, D., & Marino, F. (2013). An evolutionary optimization method for parameter search in 3D points cloud reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7995 LNCS, pp. 601–611). https://doi.org/10.1007/978-3-642-39479-9_70
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