3D Sensor-Fusion for the Documentation of Rural Heritage Buildings

3D computer vision techniques are now required for the virtual reconstruction of ancient buildings and monuments in urban environments. In this paper, we include a new subfield within the broad field of Urban Heritage that we denominate as Rural Heritage (RH), and which is focused on recovering 3D m...

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Bibliographic Details
Main Authors: F. J. Castilla, A. Ramón, A. Adán, A. Trenado, D. Fuentes
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/7/1337
Description
Summary:3D computer vision techniques are now required for the virtual reconstruction of ancient buildings and monuments in urban environments. In this paper, we include a new subfield within the broad field of Urban Heritage that we denominate as Rural Heritage (RH), and which is focused on recovering 3D models of small buildings and facilities of significance in rural environments. We, therefore, present a multi-sensory approach whose objective is to create complete architectural documentation of the dovecotes in an extended region of central Spain. This kind of aviary construction was very common in Spanish rural environments during the 19th century and the first half of the 20th century and is representative of an RH building. Sensory fusion was developed using color cameras, 3D terrestrial laser scanners, and photogrammetric techniques with Unmanned Aerial Vehicles (UAV) and achieves precise indoor and outdoor 3D models. The sensory fusion here also refers to the fact that the information coming from different sensors is integrated into a common documentation framework. A total of 80 dovecotes have been referenced and made available to the public in open access resources. The successful results and applicability of our method lead us to believe that the current documentation and the safeguard technologies in the RH field should evolve towards the use of these 3D computer vision techniques.
ISSN:2072-4292