Abstract
The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.
Highlights
In the last 20 years, various research activities, based on active and passive sensors, provided reliable methodologies for the acquisition and generation of dense point clouds and textured 3D models of heritage structures [1,2]
3D point clouds are coupled with RGB colours, intensity and other information depending on the used acquisition instrument and technique
This paper presents and evaluates an automatic classification method, based on a multi-level and multi-resolution (MLMR) approach combined with a machine learning (ML) algorithm
Summary
In the last 20 years, various research activities, based on active and passive sensors, provided reliable methodologies for the acquisition and generation of dense point clouds and textured 3D models of heritage structures [1,2] These 3D data are typically used for accurate documentation, digital preservation, and visualisation [3,4,5,6]. The identification of precise architectural components in point clouds can be very useful because it allows the direct use of 3D pint clouds for architectural interpretation inside and conservation activities planning, avoiding the modelling phase typical of HBIM (Historic Building Information Modelling) This latter phase is a time-consuming process which can lead to a great simplification of detailed surfaces, losing the metric reliability intrinsic present in the acquired point clouds
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
