Abstract
Abstract. Thanks to the recent worldwide spread of drones and to the development of structure from motion photogrammetric software, UAV photogrammetry is becoming a convenient and reliable way for the 3D documentation of built heritage. Hence, nowadays, UAV photogrammetric surveying is a common and quite standard tool for producing 3D models of relatively large areas. However, when such areas are large, then a significant part of the generated point cloud is often of minor interest. Given the necessity of efficiently dealing with storing, processing and analyzing the produced point cloud, some optimization step should be considered in order to reduce the amount of redundancy, in particular in the parts of the model that are of minor interest. Despite this can be done by means of a manual selection of such parts, an automatic selection is clearly much more viable way to speed up the final model generation. Motivated by the recent development of many semantic classification techniques, the aim of this work is investigating the use of point cloud optimization based on semantic recognition of different components in the photogrammetric 3D model. The Girifalco Fortress (Cortona, Italy) is used as case study for such investigation. The rationale of the proposed methodology is clearly that of preserving high point density in the model in the areas that describe the fortress, whereas point cloud density is dramatically reduced in vegetated and soil areas. Thanks to the implemented automatic procedure, in the considered case study, the size of the point cloud has been reduced by a factor five, approximately. It is worth to notice that such result has been obtained preserving the original point density on the fortress surfaces, hence ensuring the same capabilities of geometric analysis of the original photogrammetric model.
Highlights
Given the complexity and specificity of restoration issues in medieval fortress architectures, detailed, accurate and reliable analysis of the current status of such cultural heritage architectures should be done in order to provide appropriate information to restorers
Both UAV photogrammetry (Fig. 2 shows an orthophoto produced by properly processing UAV imagery) and Terrestrial Laser Scanning have been used in order to acquire the 3D information of interest for properly describing the fortress
This paper presented a point cloud segmentation and compression method, inspired by semantic segmentation, in order to reduce the of point cloud size while preserving most of the geometric information of interest
Summary
Given the complexity and specificity of restoration issues in medieval fortress architectures, detailed, accurate and reliable analysis of the current status of such cultural heritage architectures should be done in order to provide appropriate information to restorers. A preliminary step was the definition of the survey goals, in order to determine the building characteristics to be determined and detectable in the produced 3D model Both UAV photogrammetry (Fig. 2 shows an orthophoto produced by properly processing UAV imagery) and Terrestrial Laser Scanning have been used in order to acquire the 3D information of interest for properly describing the fortress. In particular, the produced dataset is optimized in order to ease the automatic extraction of information about the building walls This kind of procedure can be useful for instance for the automatic generation of semantic models (after a proper segmentation and classification (Vosselman et al, 2004, Rabbani et al, 2006, Makuti et al, 2018)), e.g. BIMs and CityGML models.
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