In this paper, we present a method developed in order to increase the accuracy of the sparse point cloud (made of Tie Points) created from images acquired from UAVs (Unmanned Aerial Vehicles). In a first step, after having aligned the images, the probability distribution of reprojection errors on each single image is statistically analyzed, then masks are automatically created to remove image regions characterized by reprojection error values outside the chosen confidence interval. The re-sults are promising, and highlight the usefulness of using input data preparation techniques to in-crease the accuracy of the output 3D model.

AUTOMATIC POINT CLOUD EDITING FROM UAV AERIAL IMAGES: APPLICATIONS IN ARCHAEOLOGY AND CULTURAL HERITAGE

SALVATORE BARBA;ALESSANDRO DI BENEDETTO;MARGHERITA FIANI;LUCAS GUJSKI;MARCO LIMONGIELLO
2022-01-01

Abstract

In this paper, we present a method developed in order to increase the accuracy of the sparse point cloud (made of Tie Points) created from images acquired from UAVs (Unmanned Aerial Vehicles). In a first step, after having aligned the images, the probability distribution of reprojection errors on each single image is statistically analyzed, then masks are automatically created to remove image regions characterized by reprojection error values outside the chosen confidence interval. The re-sults are promising, and highlight the usefulness of using input data preparation techniques to in-crease the accuracy of the output 3D model.
2022
978-88-6952-159-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4814293
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