Point cloud optimization based on 3D geometric features for architectural heritage modelling
[EN] The present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing ste...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Ajuntament de Barcelona |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/17535 |
| Acceso en línea: | https://hdl.handle.net/10612/17535 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingenierías Classification Optimization Cultural heritage Point Cloud Geometrical features |
| Sumario: | [EN] The present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing step for architectural heritage modelling. A point cloud classification that optimizes the point cloud while preserving the relevant information will improve the subsequent operations. The present methodology is based on the extraction of the geometric properties of the 3D point clouds on the basis of the 3D covariance matrix. Among all the possible dimensional features, the omnivariance (Ω) is considered the most suitable for the variety of situations of the architectural heritage elements. For a study case of the Niculoso Pisano Portal of the Monastery of Santa Paula of Seville (Spain), three clusters are defined according to the different level of details. As a result, and in comparison, to a standard spatial sampling of 1 cm, the proposed clustering allowed a weight spatial sampling within the interval 20 – 1 cm, achieving an 85%-point reduction, keeping 3D points in the complex areas, whereas the low detail areas, like planes, were considerably reduced in size for the next steps of parametric modelling. The error of the optimized point cloud, by the comparison with the original point cloud has a mean value of 0.3 mm and a standard deviation of ± 4.6 mm. |
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