Abstract
Abstract. In this paper we present an automated method to derive highly detailed 3D vector models of modern building façades from terrestrial laser scanning data. The developed procedure can be divided into two main steps: firstly the main elements constituting the façade are identified by means of a segmentation process, then the 3D vector model is generated including some priors on architectural scenes. The identification of main façade elements is based on random sampling and detection of planar elements including topology information in the process to reduce under- and over-segmentation problems. Finally, the prevalence of straight lines and orthogonal intersections in the vector model generation phase is exploited to set additional constraints to enforce automated modeling. Contemporary a further classification is performed, enriching the data with semantics by means of a classification tree. The main application field for these vector models is the design of external insulation thermal retrofit. In particular, in this paper we present a possible application for energy efficiency evaluation of buildings by mean of Infrared Thermography data overlaid to the façade model.
Highlights
Due to the increasing capabilities of terrestrial laser scanning (TLS) technology during the acquisition phase, geometric models are rapidly growing in size and complexity
The goal of this paper is to present an automatic approach for planar object extraction and 3D model generation starting from point clouds of building façades acquired by TLS
The developed approach for façade modeling allows for automatic planar object extraction and 3D vector model generation starting from point clouds of building façades acquired with TLS
Summary
Due to the increasing capabilities of terrestrial laser scanning (TLS) technology during the acquisition phase, geometric models are rapidly growing in size and complexity. Nowadays an increasing interest is paid to the generation of detailed building models from TLS data (Becker and Haala, 2007) This is due to the fact that the raw point cloud generation process is quite simple and highly automated, reducing this way the time for data acquisition and registration. Further problems arise due to the huge size of data to be managed For these reasons the point cloud is generally vectorized, and possibly enriched with semantic information, allowing in this way an higher-level of interaction to the user. Operations such as editing, moving and replacing objects in the vectorized model, make the data more usable for modelling purposes. Bad-segmentation results may be categorized into: i. under-segmentation, in the case several features are segmented as one; ii. over-segmentation, one feature is segmented into several
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 callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: 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.
