Volume XLII-2/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 81-86, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-81-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 81-86, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-81-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jan 2019

31 Jan 2019

VAULT MODELING WITH NEURAL NETWORKS

L. Barazzetti and M. Previtali L. Barazzetti and M. Previtali
  • Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering, Via Ponzio 31, 20133 Milano, Italy

Keywords: Automation, Machine learning, Modeling, Neural Network, Point cloud, Vault

Abstract. Nowadays, the digital reconstruction of vaults is carried out using photogrammetric and laser scanning techniques able to capture the visible surface with dense point clouds. Then, different modeling strategies allow the generation of 3D models in various formats, such as meshes that interpolates the acquired point cloud, NURBS-based reconstructions based on manual, semi-automated, or automated procedures, and parametric objects for Building Information Modeling. This paper proposes a novel method that reconstructs the visible surface of a vault using neural networks. It is based on the assumption that vaults are not irregular free-form objects, but they can be reconstructed by mathematical functions calculated from the acquired point clouds. The proposed approach uses the point cloud to train a neural network that approximates vault surface. The achieved solution is not only able to consider the basic geometry of the vault, but also its irregularities that cannot be neglected in the case of accurate and detailed modeling projects of historical vaults. Considerations on the approximation capabilities of neural networks are illustrated and discussed along with the advantages of creating a mathematical representation encapsulated into a function.