Open AccessFlexibility of Dense 3D Data Capture
Author(s) -
Martina Attenni,
Marika Griffo,
Carlo Inglese,
Alfonso Ippolito,
Eric Lo,
Dominique Rissolo
Publication year - 2021
Publication title -
studies in digital heritage
Language(s) - English
Resource type - Journals
ISSN - 2574-1748
DOI - 10.14434/sdh.v5i1.31442
Subject(s) - computer science , flexibility (engineering) , data science , spatial analysis , optimal distinctiveness theory , data mining , object (grammar) , data structure , artificial intelligence , geography , psychology , statistics , remote sensing , mathematics , psychotherapist , programming language
The knowledge and study of built heritage is now deeply connected to methodologies associated with the capture of surface details via the production of point-data. These methodologies enable researchers to gather a wider range of information, which is increasingly more connected to technological advances. Such approaches influence the management of data, and these data are often redundant due to the ways in which they are captured. Massive data capture does not include preliminary selection based on metric, geometric, and material features of the object. A multi-scalar approach, in which the criteria for data capture depends on the goals of the survey, is needed to optimize the relationship between information and the scale of the models to be built. This case study involving a selection of fountains in Rome aims to apply these principles to urban contexts defined by a strong spatial connection between architectural and sculptural elements. Survey can express this distinctiveness through complex, dynamic, and effective digital models.