Accurate 3d Reconstruction Using a Videogrammetric Device for Heritage Scenarios

Abstract

Abstract. In recent years, handheld laser scanning systems have been developed for documenting architectural heritage, among other applications. In this article we present a new alternative for the 3D documentation of historical heritage based on videogrammetry. For this purpose, a prototype has been designed with two cameras, a high resolution camera and a VGA camera which, when connected to a tablet, allow the user to establish a guidance system to ensure that the trajectory is not lost and enables highly flexible and long-lasting movements over time. This paper unveils the operation of the filtering and image selection system to finally proceed to an evaluation of the prototype in three areas of an archaeological site, called “Casa del Mitreo” in the city of Mérida (Spain). The results are compared to the Faro Focus 3D X330 laser scanner, yielding very similar accuracies and a capture time about 17 times faster than the 3D laser scanner. The article therefore proposes a real alternative to 3D data acquisition systems in applications for the graphic documentation of architectural and archaeological heritage.

A Rapid Pipeline for Periodic Inspection and Maintenance of Architectural Surfaces

“…For the benefit of simple and expeditious procedures toward data collection, a further strategy is emerging, which entails rapid smartphone video-acquisition, for the retrieval of multi-image 3D reconstructions (Sirmacek & Lindenbergh, 2014), (Repola et al, 2018) (Sun & Zhang, 2019), with the purpose of assessing their morphology and conditions (Torresani & Remondino, 2019) (Luigi Barazzetti et al, 2020) (Costantino et al, 2022). Indeed, in recent years, accurate 3D reconstruction of heritage assets has been realized, through the use of high-resolution smartphone-based videogrammetry, which demonstrates the suitability of this kind of output, with respect to heritage documentation (Murtiyoso & Grussenmeyer, 2021), (Ortiz-Coder & Cabecera, 2021a), (Ortiz-Coder & Cabecera, 2021b). Another line of research focuses on the implementation of supervised/unsupervised machine learning systems for the automatic segmentation or classification of 2D or 3D outputs, such as, on the one hand, UV maps, orthoimages or textures, and, on the other hand, point clouds.…”