AUTOMATIC HERITAGE BUILDING POINT CLOUD SEGMENTATION AND CLASSIFICATION USING GEOMETRICAL RULES
<p>The segmentation of a point cloud presents an important step in the 3D modelling process of heritage structures. This is true in many scale levels, including the segmentation, identification, and classification of architectural elements from the point cloud of a building. In this regard,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W15/821/2019/isprs-archives-XLII-2-W15-821-2019.pdf |
| Summary: | <p>The segmentation of a point cloud presents an important step in the 3D modelling process of heritage structures. This is true in many
scale levels, including the segmentation, identification, and classification of architectural elements from the point cloud of a
building. In this regard, historical buildings often present complex elements which render the 3D modelling process longer when
performed manually. The aim of this paper is to explore approaches based on certain common geometric rules in order to segment,
identify, and classify point clouds into architectural elements. In particular, the detection of attics and structural supports (i.e.
columns and piers) will be addressed. Results show that the developed algorithm manages to detect supports in three separate data
sets representing three different types of architecture. The algorithm also managed to identify the type of support and divide them
into two groups: columns and piers. Overall, the developed method provides a fast and simple approach to classify point clouds
automatically into several classes, with a mean success rate of 81.61 % and median success rate of 85.61&thinsp% for three tested data sets.</p> |
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| ISSN: | 1682-1750 2194-9034 |