Abstract
Abstract. The use of Structure-from-Motion algorithms is a common practice to obtain a rapid photogrammetric reconstruction. However, the performance of these algorithms is limited by the fact that in some conditions the resulting point clouds present low density. This is the case when processing materials from historical archives, such as photographs and videos, which generates only sparse point clouds due to the lack of necessary information in the photogrammetric reconstruction. This paper explores ways to improve the performance of open source SfM algorithms in order to guarantee the presence of strategic feature points in the resulting point cloud, even if sparse. To reach this objective, a photogrammetric workflow is proposed to process historical images. The first part of the workflow presents a method that allows the manual selection of feature points during the photogrammetric process. The second part evaluates the metric quality of the reconstruction on the basis of a comparison with a point cloud that has a different density from the sparse point cloud. The workflow was applied to two different case studies. Transformations of wall paintings of the Karanlık church in Cappadocia were analysed thanks to the comparison of 3D model resulting from archive photographs and a recent survey. Then a comparison was performed between the state of the Komise building in Japan, before and after restoration. The findings show that the method applied allows the metric scale and evaluation of the model also in bad condition and when only low-density point clouds are available. Moreover, this tool should be of great use for both art and architecture historians and geomatics experts, to study the evolution of Cultural Heritage.
Highlights
Point cloud comparison is a method sometimes used to evaluate the metric quality of the 3D reconstruction process in photogrammetry
If point clouds from a recent survey are available, the comparison between this point cloud and the one resulting from the photogrammetric processing of historical photographs could fail due to fact that the few points of the sparse cloud do not correspond with the ones of the dense point cloud
With the “Feature point selection” step, introduced in this research, it is possible to manual detected the feature point of interest
Summary
Point cloud comparison is a method sometimes used to evaluate the metric quality of the 3D reconstruction process in photogrammetry. Point clouds, coming from different primary data and/or techniques (e.g. the ones coming out from photogrammetric survey and the ones coming out from a laser scanning process), could vary greatly in their point densities and their accuracies. This is due to the intrinsic characteristics of the instruments, the sensor size and the distance between sensor and object (Bracci et al, 2018). The way in which the data is acquired can cause noisy results and blunders especially when different platforms or lowcost sensors (Byrne et al, 2017) are employed, due to scale and illumination changes or quality and quantity of single sources (Farella et al, 2019)
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