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A General Approach for the Reconstruction of Complex Buildings from 3D Point Clouds Using Bayesian Networks and Cellular Automata
Chizhova, Maria; Korovin, Dmitrii; Gurianov, Andrey; u. a. (2018): A General Approach for the Reconstruction of Complex Buildings from 3D Point Clouds Using Bayesian Networks and Cellular Automata, in: Fabio Remondino, Andreas Georgopoulos, Diego Gonzalez-Aguilera, u. a. (Hrsg.), Latest Developments in Reality-Based 3D Surveying and Modelling, Basel: MDPI, S. 74–92, doi: 10.3390/books978-3-03842-685-1-4.
Faculty/Chair:
Author: ;  ;  ;  ;  ; 
Title of the compilation:
Latest Developments in Reality-Based 3D Surveying and Modelling
Editors:
Remondino, Fabio
Georgopoulos, Andreas
Gonzalez-Aguilera, Diego
Agrafiotis, Panagiotis
Publisher Information:
Year of publication:
2018
Pages:
ISBN:
9783038426851
9783038426844
Language:
English
Abstract:
Point cloud interpretation and reconstruction of 3D buildings from point clouds has already been addressed for a few decades. There are many articles which consider different methods and workflows of the automatic detection and reconstruction of geometrical objects from point clouds. Each method is suitable for a specific 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 sets of geometric objects to be reconstructed, regardless of the level of damage. In a real example, we reconstruct a complete Russian Orthodox church starting from a set of detected structural components and reconstruct missing components with high probability.
Keywords: ;  ; 
reconstruction
cellular automata
Bayesian networks
Type:
Contribution to an Articlecollection
Activation date:
June 27, 2025
Versioning
Question on publication
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https://fis.uni-bamberg.de/handle/uniba/108791