@inproceedings{10.1007/978-3-032-06389-2_15,
title = {Deep Learning-Based Monocular Depth Estimation in Cultural Heritage},
author = {Vasileios Arampatzakis and Fotis Arnaoutoglou and Anestis Koutsoudis and George Pavlidis},
editor = {George Pavlidis and Stella Sylaiou},
url = {https://link.springer.com/chapter/10.1007/978-3-032-06389-2_15},
isbn = {978-3-032-06389-2},
year = {2025},
date = {2025-12-31},
urldate = {2025-12-31},
booktitle = {Transforming Heritage Research in a Transforming World: 5th CAA-GR Conference 2024},
pages = {155–163},
publisher = {Springer},
address = {Cham},
abstract = {In cultural heritage, various methods have addressed three-dimensional (3D) digitization and reconstruction using two-dimensional (2D) images, including shape from structured light, shape from stereo, and structure from motion. A common challenge is the recognition of the distance of objects in a scene from the viewpoint, usually called depth. Recognizing depth in 2D photographs remains a challenge in computer vision. Deep Learning techniques have improved this area, particularly in what is called monocular depth estimation. This work investigates the applicability of state-of-the-art monocular depth estimation methods as a preliminary step toward complete and automated 3D reconstruction of cultural heritage artifacts. As image-based 3D reconstruction is extremely time-consuming and expertise-demanding, monocular depth estimation methods could provide significant advantages in speeding up the overall process. Our preliminary experiments yield promising results, with a mean accuracy of 88.15%, a mean RMSE error of 37.35, and a mean PSNR of 27.37 dB across these methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
