Abstract
During the past few years, new developments have occurred in the field of 3D photogrammetric modeling of culture heritage. One of these developments is the expansion of 3D photogrammetric modeling open-source software, such as VisualSfM, and cost-effective licensed software, such as Agisoft Metashape into the practical and affordable world. This type of SfM (Structure from Motion) software offers the world of 3D modelling of culture heritage a powerful tool for documentation and visualization. On the other hand, low-cost cameras are now available on the market. These cameras are characterized by high resolution and good quality lens, which makes them suitable for photogrammetric modelling. This paper reports on the results of the application of a SfM photogrammetry system in the 3D modelling of Safita Tower, a medieval structure in Safita, north-western Syria. The applied photogrammetric system consists of the Nikon Coolpix P100 10 MP digital camera and the commercial software Agisoft Metashape. The resulted 3D point clouds were compared with an available dense point cloud acquired by a laser scanner. This comparison proved that the low-cost SfM photogrammetry is an accurate methodology to 3D modeling historical monuments.
Highlights
Historical monuments are of particular importance as they represent the memory and history of the countries where they are placed
3D laser scanning and SfM Photogrammetry are the main techniques used to record the geometry of historical monuments, in the form of textured 3D point cloud [3]
This model is known as a Digital Surface Model (DSM)
Summary
Historical monuments are of particular importance as they represent the memory and history of the countries where they are placed. These monuments should be protected to prevent their deterioration and destruction [1]. 3D laser scanning and SfM Photogrammetry are the main techniques used to record the geometry of historical monuments, in the form of textured 3D point cloud [3]. SfM Photogrammetry involves acquiring images from several positions relative to the studied object. An algorithm, such as the scaleinvariant feature transform (SIFT) identifies distinctive features appearing upon multiple images and establishes the spatial relationships between the original camera positions in an arbitrary and unscaled coordinate system. A bundle adjustment is applied to extract a sparse set of coordinates to represent the object [4], [5]
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