PHOTOGRAMMETRY: EVOLUTION, CURRENT STATE, AND FUTURE TRAJECTORIES WITH A FOCUS ON BIM INTEGRATION

Abstract

BIM (Building Information Modeling) is considered one of the most advanced systems currently available for managing and monitoring engineering projects across all their phases and typologies. The system derives its strength from cutting-edge field data acquisition and processing technologies, such as LiDAR and XR. However, most of these technologies remain prohibitively expensive, particularly for small-scale projects. This is where photogrammetry emerges as a relatively cost-effective alternative. This thesis aims to clarify this technology and assess its potential to support BIM workflows, by examining its various types, stages of development, and underlying mathematical models, while identifying the advantages of each approach. To this end, field imagery was processed using three different photogrammetric methods SfM, NeRF, and Gaussian Splatting employing various software packages and computer systems with differing specifications. The results revealed no significant difference in the field image acquisition process; however, the data processing pipelines and resulting outputs varied considerably. While SfM leads as the foundational method for all photogrammetric approaches, offering the highest geometric accuracy in 3D models, Gaussian Splatting excels in the visual realism of its outputs, albeit at the cost of larger file sizes. NeRF ،on the other hand, produces AI-generated models with impressive visual appearance, but lacks true geometric structure, rendering it unsuitable for precise engineering measurements.