The geometry of an object can be efficiently captured using digital surveying techniques. By modelling and visualization of 3D data, it is possible to obtain vectorized information. Many users prefer to work on profiles, motivated by the need to produce two-dimensional documentation. Profiles extracted from the point clouds will inherit the noise characteristics of the original models. The application of appropriate filters can reduce this adverse effect. In general, the quality of raw data is highly dependent on the equipment performance and the survey design. This work presents an approach to the vectorization of profiles extracted from complete clouds, evaluating the potential of automatic solutions through the comparison with manually produced drawings. The discrepancy between automatically extracted contours and handheld vectorized lines highlights that the tested concave hull algorithm is able to accurately extrapolate the contours of the sectioned cloud, resulting in compression of the processing time, if the raw input data does not present serious gaps or exces- sive noise. Hausdorff distance between homologous models is equal to 1.66 cm, compatible with the degree of relative definition for 1:50 scale outputs, with considerable margins for improvement through optimization of the detection phases.

An Approach to Vector Data Extraction from 3D Point Clouds. The Paleochristian Baptistery of Santa Maria Maggiore

Andrea di Filippo;Barbara Messina
2020

Abstract

The geometry of an object can be efficiently captured using digital surveying techniques. By modelling and visualization of 3D data, it is possible to obtain vectorized information. Many users prefer to work on profiles, motivated by the need to produce two-dimensional documentation. Profiles extracted from the point clouds will inherit the noise characteristics of the original models. The application of appropriate filters can reduce this adverse effect. In general, the quality of raw data is highly dependent on the equipment performance and the survey design. This work presents an approach to the vectorization of profiles extracted from complete clouds, evaluating the potential of automatic solutions through the comparison with manually produced drawings. The discrepancy between automatically extracted contours and handheld vectorized lines highlights that the tested concave hull algorithm is able to accurately extrapolate the contours of the sectioned cloud, resulting in compression of the processing time, if the raw input data does not present serious gaps or exces- sive noise. Hausdorff distance between homologous models is equal to 1.66 cm, compatible with the degree of relative definition for 1:50 scale outputs, with considerable margins for improvement through optimization of the detection phases.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4750597
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