Five Keystones Vaults Parametric Model Generation from Point Cloud Data
- Autores: Mara Capone, Daniela Palomba, Emanuela Lanzara
- Localización: Architectural Graphics / coord. por Manuel Alejandro Ródenas López, José Calvo López, Macarena Salcedo Galera, Vol. 3, 2022 (Graphics for Education and Thought), ISBN 9783031046407, págs. 271-280
- Idioma: inglés
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Resumen
The definition of an adaptive parametric models to automate modelling architectural elements is one of the main goal for heritage documentation, dissemination and management in HBIM process. Parametric objects library of historical architectural elements is a very important step in more than one process, starting from 3D Point Clouds segmentation with Deep Learning (DL) techniques to 3D modelling from Point Cloud. Following the state of the art, we have defined an adaptive parametric model for ribbed vaults modelling that allows you to generate a 3D model based on geometric rules and\or from point cloud data. Starting from geometric rules we defined the main parameters to use to generate different “ideal models” and the specific parameters from point cloud data to use to generate a “reality based model”. We compared the different 3D models based on geometric rules from literature, 3D reality based model with mesh model from point cloud data in order to evaluate the process, to improve it and to verify the design hypothesis. We tested this workflow on a case study: Saint-Eustache church in Paris. There are some different kind of star vaults in Saint-Eustache, in this paper we show the parametric model for five keystones generation based on geometric rules from French literature and from point cloud data.
