scholarly journals Assessment of the quality of as-is building information models generated from point clouds using deviation analysis

2011 ◽  
Author(s):  
Engin Burak Anil ◽  
Pingbo Tang ◽  
Burcu Akinci ◽  
Daniel Huber
Keyword(s):  
Point Clouds ◽  
Deviation Analysis ◽  
Information Models ◽  
Building Information ◽  
Building Information Models

scholarly journals ASSESSMENT OF A KEYPOINTS DETECTOR FOR THE REGISTRATION OF INDOOR AND OUTDOOR HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 133-138
Author(s):  
R. Assi ◽  
T. Landes ◽  
A. Murtiyoso ◽  
P. Grussenmeyer
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Zoological Museum ◽  
Registration Method ◽  
Difficult Time ◽  
Information Models ◽  
Building Information ◽  
Special Points ◽  
Indoor And Outdoor

<p><strong>Abstract.</strong> In the context of architectural heritage preservation, constructing building information models is an important task. However, conceiving a pertinent model is a difficult, time consuming and user-dependent task. Our laboratory has been researching methods to decrease the time and errors inferred by manual segmentation of point clouds. In the perspective of automatization of the process, we implemented an automated registration method that used only keypoints. Keypoints are special points that hold more information about the global structure of the cloud. In order to detect keypoints, we used the Point Cloud Library (PCL) toolbox. The pertinence of the method was evaluated by registering more than 300 clouds of the zoological museum of Strasbourg. The quality of the keypoint detection was first verified on geo-referenced indoor point clouds. Then we applied this method to register the indoor and outdoor point clouds that have much less area in common; those common points being generally the doors and windows of the façade. The registrations of indoor point clouds were satisfying, with mean distances to the ground truth inferior to 20&amp;thinsp;cm. While the first result for joint indoor/outdoor registration are promising, it may be improved in future works.</p>

scholarly journals COMPARISON AND ASSESSMENT OF 3D REGISTRATION AND GEOREFERENCING APPROACHES OF POINT CLOUDS IN THE CASE OF EXTERIOR AND INTERIOR HERITAGE BUILDING RECORDING

2018 ◽  
Vol XLII-2 ◽  
pp. 745-751 ◽  
Author(s):  
A. Murtiyoso ◽  
P. Grussenmeyer
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
3D Registration ◽  
Technical Problems ◽  
Information Models ◽  
Geometrical Quality ◽  
Building Information ◽  
Heritage Building ◽  
Free Network

In the field of 3D heritage documentation, point cloud registration is a relatively common issue. With rising needs for Historic Building Information Models (HBIMs), this issue has become more important as it determines the quality of the data to be used for HBIM modelling. Furthermore, in the context of historical buildings, it is often interesting to document both the exterior façades as well as the interior. This paper will discuss two approaches of the registration and georeferencing of building exterior and interior point clouds coming from different sensors, namely the independent georeferencing method and the free-network registration and georeferencing. Building openings (mainly windows) were used to establish common points between the systems. These two methods will be compared in terms of geometrical quality, while technical problems in performing them will also be discussed. Furthermore, an attempt to automate some parts of the workflow using automatic 3D keypoints and features detection and matching will also be described in the paper. Results show that while both approaches give similar results, the independent approach requires less work to perform. However, the free-network method has the advantage of being able to compensate for any systematic georeferencing error on either system. As regards to the automation attempt, the use of 3D keypoints and features may reduce processing time; however correct tie point correspondence filtering remains difficult in the presence of heavy point cloud noise.

scholarly journals STOCHASTIC AND GEOMETRIC REASONING FOR INDOOR BUILDING MODELS WITH ELECTRIC INSTALLATIONS – BRIDGING THE GAP BETWEEN GIS AND BIM

2017 ◽  
Vol IV-4/W5 ◽  
pp. 33-39
Author(s):  
Y. Dehbi ◽  
J.-H. Haunert ◽  
L. Plümer
Keyword(s):  
Graphical Models ◽  
Building Design ◽  
Point Clouds ◽  
Linear Constraints ◽  
Indoor Environments ◽  
3D Point Clouds ◽  
Building Models ◽  
Information Models ◽  
Building Information ◽  
Building Information Models

3D city and building models according to CityGML encode the geometry, represent the structure and model semantically relevant building parts such as doors, windows and balconies. Building information models support the building design, construction and the facility management. In contrast to CityGML, they include also objects which cannot be observed from the outside. The three dimensional indoor models characterize a missing link between both worlds. Their derivation, however, is expensive. The semantic automatic interpretation of 3D point clouds of indoor environments is a methodically demanding task. The data acquisition is costly and difficult. The laser scanners and image-based methods require the access to every room. Based on an approach which does not require an additional geometry acquisition of building indoors, we propose an attempt for filling the gaps between 3D building models and building information models. Based on sparse observations such as the building footprint and room areas, 3D indoor models are generated using combinatorial and stochastic reasoning. The derived models are expanded by a-priori not observable structures such as electric installation. Gaussian mixtures, linear and bi-linear constraints are used to represent the background knowledge and structural regularities. The derivation of hypothesised models is performed by stochastic reasoning using graphical models, Gauss-Markov models and MAP-estimators.

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