Abstract
This paper presents a data-driven free-form modelling method dedicated to the parametric modelling of buildings with complex shapes located in particularly valuable Old Town Centres, using Airborne LiDAR Scanning (ALS) data and aerial imagery. The method aims to reconstruct and preserve the input point cloud based on the relative density of the data. The method is based on geometric operations, iterative transformations between point clouds, meshes, and shape identification. The method was applied on a few buildings located in the Old Town Centre of Bordeaux (France). The 3D model produced shows a mean distance to the point cloud of 0.058 m and a standard deviation of 0.664 m. In addition, the incidence of building footprint segmentation techniques in automatic and interactive model-driven modelling was investigated and, in order to identify the best approach, six different segmentation methods were tested. The segmentation was performed based on the footprints derived from Digital Surface Model (DSM), point cloud, nadir images, and OpenStreetMap (OSM). The comparison between the models shows that the segmentation that produces the most accurate and precise model is the interactive segmentation based on nadir images. This research also shows that in modelling complex structures, the model-driven method can achieve high levels of accuracy by including an interactive editing phase in building 3D models.
Highlights
IntroductionThree-dimensional (3D) building models play a key role in many applications related to the study of cities, such as urban planning, calculation of solar radiation, analysis of the impact of shadows, assessment of visibility, mapping, creation of emergency plans, cadastral inventories, creation of virtual environments for entertainment, etc
Three-dimensional (3D) building models play a key role in many applications related to the study of cities, such as urban planning, calculation of solar radiation, analysis of the impact of shadows, assessment of visibility, mapping, creation of emergency plans, cadastral inventories, creation of virtual environments for entertainment, etc. [1,2,3,4].In recent decades, point clouds have emerged as one of the major input data for 3D city modelling [5]
We have developed a method for the reconstruction of historical buildings with a complex structure based on data-driven free-form and model-driven modelling; in particular, we used the point cloud generated by hybrid sensors, i.e., sensors able to acquire Airborne LiDAR Scanning (ALS) data and photographic images
Summary
Three-dimensional (3D) building models play a key role in many applications related to the study of cities, such as urban planning, calculation of solar radiation, analysis of the impact of shadows, assessment of visibility, mapping, creation of emergency plans, cadastral inventories, creation of virtual environments for entertainment, etc. Point clouds have emerged as one of the major input data for 3D city modelling [5]. Point clouds are mainly collected and generated by LiDAR (Light Detection and Ranging) scanning and photogrammetric techniques. Point cloud post-processing is a multidisciplinary research field involving, for example, geomatics, computer graphics, computer vision, artificial intelligence, architecture, and others [5,6,7,8,9]. In 3D building modelling, MLS and TLS data are used for the construction of façades, and ALS data can be used for modelling the whole buildings but are essential in roof modelling
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