Article
Version 1
Preserved in Portico This version is not peer-reviewed
Parametric Point Cloud Slicing for Facade Retrofitting
Version 1
: Received: 3 July 2021 / Approved: 5 July 2021 / Online: 5 July 2021 (11:15:12 CEST)
How to cite: Gámez Bohórquez, O.; Derigent, W.; Bril El Haouzi, H. Parametric Point Cloud Slicing for Facade Retrofitting. Preprints 2021, 2021070092. https://doi.org/10.20944/preprints202107.0092.v1 Gámez Bohórquez, O.; Derigent, W.; Bril El Haouzi, H. Parametric Point Cloud Slicing for Facade Retrofitting. Preprints 2021, 2021070092. https://doi.org/10.20944/preprints202107.0092.v1
Abstract
Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.
Keywords
Facade retrofitting; Scan to BIM; Parametric modelling; Terrestrial laser scanning; Building life cycle.
Subject
Engineering, Automotive Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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