Automatic Recognition and Digital Documentation of Cultural Heritage Hemispherical Domes using Images

Recent advancements in optical metrology have enabled continuous documentation of dense 3-dimensional (3D) point clouds of construction projects, including cultural heritage preservation projects. These point clouds must then be further processed to generate semantic digital models, which is integral to the lifecycle management of heritage sites. For large-scale and continuous digital documentation, processing of dense 3D point clouds is computationally cumbersome, and consequentially requires additional hardware for data management and analysis, increasing the time, cost, and complexity of the project. Fast and reliable solutions for generating the geometric digital models is, hence, eminently desirable. This article presents an original approach to generate reliable semantic digital models of heritage hemispherical domes using only two images. New closed formulations were derived to establish the relationships between a sphere and its projected ellipse onto an image. These formulations were then utilised to create new methods for: (i) selecting the best pair of images from an image network; (ii) detecting ellipses corresponding to projection of spheres in images; (iii) matching of the detected ellipses between images; and (iv) generating the sphere's geometric digital models. The effectiveness of the proposed method was evaluated under both laboratory and real-world datasets. Laboratory experiments revealed that the proposed process using the best pair of images provided results as accurate as that achieved using eight randomly selected images, while improving computation time by a factor of 50. The results of the two real-world datasets showed that the digital model of a hemispherical dome was generated with 6.2 mm accuracy, while improving the total computation time of current best practice by a factor of 7. Real-world experimentation also showed that the proposed method can provide metric-scale definition for photogrammetric point clouds with 3 mm accuracy using spherical targets. The results suggest that the proposed method was successful in automatically generating fast and accurate geometric digital models of hemispherical domes.