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The cultural human heritage is important for the identity of following generations and has to be preserved in a suitable manner. In the course of time a lot of information about former cultural constructions has been lost because some objects were strongly damaged by natural erosion or on account of human work or were even destroyed. It is important to capture still available building parts of former buildings, mostly ruins. This data could be the basis for a virtual reconstruction. Laserscanning offers in principle the possibility to take up extensively surfaces of buildings in its actual status.
In this paper we assume a priori given 3d-laserscanner data, 3d point cloud for the partly destroyed church. There are many well known algorithms, that describe different methods of extraction and detection of geometric primitives, which are recognized separately in 3d points clouds. In our work we put them in a common probabilistic framework, which guides the complete reconstruction process of complex buildings, in our case russian-orthodox churches.
Churches are modeled with their functional volumetric components, enriched with a priori known probabilities, which are deduced from a database of russian-orthodox churches. Each set of components represents a complete church. The power of the new method is shown for a simulated dataset of 100 russian-orthodox churches.
Dieser Beitrag handelt von einer Methode zur Änderungsdetektion im Gebäudeinneren, die auf der Diskretisierung des Objektraums durch Voxel basiert. Die Belegung des Voxelraums wird anhand von Punktwolken aus terrestrischem Laserscanning zu verschiedenen Bauphasen ermittelt. Durch die Aufdeckung von Belegungskonflikten zwischen zwei Messepochen wird ermittelt, ob Änderungen an einer bestimmten Stelle aufgetreten sind. Es
wird gezeigt, wie sich der Diskretisierungsfehler als Resultat der Voxelisierung auf die noch erreichbare räumliche Auflösung einer Änderungsaussage auswirkt. Am Beispiel von Abbrucharbeiten bei einem Innenumbauprojekt werden die wechselseitigen Auswirkungen von Aufnahmegeometrie, Scanauflösung und Voxelgröße diskutiert. Die Ergebnisse zeigen, dass die Auflösung von 3D-Punktwolken unter Beachtung einiger Anforderungen an die Aufnahmegeometrie bei Innenraum-Messungen ausreicht, um bei gängigen Genauigkeitsanforderungen auf Voxelebene automatisiert strukturelle Änderungen zu beschreiben.
Photogrammetric point clouds offer immense potential for various applications, especially for the AEC industry and ”as-built” BIM. However, despite many advantages such as time and cost efficiency, image based point clouds of indoor environments mostly suffer from inhomogeneous and strongly fluctuating point-wise uncertainties. This lack of area-filling geometric reliability represents a strong barrier for innovations and further development of image based applications for as-built BIM, regarding both software and hardware. Therefore, this paper presents a method for the geometric verification of indoor BIMs by images and uncertainty management in order to unleash the potential of photogrammetry in context of professional building documentation heading towards ”digital twinning”. Individual 3D point accuracies, object’s surface characteristics and BIM related uncertainties according to the Level of Accuracy (LOA) specification are assessed and taken into account. The final decision of whether or not a photogrammetric point cloud confirms a given model within its associated level of accuracy results from a combined reasoning pipeline based on Dempster–Shafer evidence theory. The novel Pho-to-BIM verification method is demonstrated on three real indoor construction sites, each 3D mapped with different image sensors. Based on the experiments it is shown how to set up belief functions for evidence based reasoning individually, depending on the measurement and site characteristics.
Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestrial laser scanning (TLS) and the discretization of space by voxels. In order to evaluate the metric accuracy of a BIM according to the Level of Accuracy (LOA) specification, the effects of laser range measurements on the occupancy of space are modeled with belief functions and evaluated using Dempster and Shafer's theory of evidence. The application is demonstrated on the point cloud data of multi temporal scanning campaigns of real indoor reconstructions. The results show that TLS point clouds are suitable to verify a given BIM up to LOA 40 if special attention is paid to the scanning geometry during the acquisition. The proposed method can be used to document construction progress, verify and even update the LOA status of a given BIM, confirming valid and BIM-compliant as-built models for further planning.
For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).
The point cloud interpretation and reconstruction of 3d-buildings from point clouds has already been treated for a few decades. There are many articles which consider the different methods and workows of the automatic detection and reconstruction of geometrical objects from point clouds. Each method is suitable for the special geometry type of object or sensor. General approaches are rare. In our work we present an algorithm which develops the optimal process sequence of the automatic search, detection and reconstruction of buildings and building components from a point cloud. It can be used for the detection of the set of geometric objects to be reconstructed, independent of its destruction. In a simulated example we reconstruct a complete Russian-orthodox church starting from the set of detected structural components and reconstruct missing components with high probability.
Complex geometry extraction from point clouds is an actual problem in reverse engineering. Simple geometrical models (like parallelepipeds, prisms, pyramids, cones, spheres) were already applied in construction and machine-building modeling, but are not sufficient for high quality BIM now. This work, which is carried out in the context of virtual reconstruction of destroyed orthodox churches, presents a robust and efficient method of cupola (domes) and tambour geometry extraction from precise point clouds. The rich diversity of architectural forms, which are defined by many parameters, does not allow to consider this problem as a trivial duty, because usual geometry extraction methods fail for these object types. The new developed algorithm is presented and realized