@article{ChizhovaGurianovHessetal., author = {Chizhova, Maria and Gurianov, Andrey and Hess, Mona and Luhmann, Thomas and Brunn, Ansgar and Stilla, Uwe}, title = {Semantic segmentation of building elements using point cloud hashing}, series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {VLII-2}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {https://doi.org/10.5194/isprs-archives-XLII-2-241-2018}, pages = {241 -- 250}, abstract = {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).}, language = {en} } @article{ChizhovaKorovinGurianovetal., author = {Chizhova, Maria and Korovin, Dmitriyi and Gurianov, Andrey and Brodovskii, Maxim and Brunn, Ansgar and Stilla, Uwe and Luhmann, Thomas}, title = {Probabilistic Reconstruction of orthodox Churches from precision Point Clouds using Bayesian Networks and Cellular Automata}, series = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {VLII-2/W3}, journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {doi:10.5194/isprs-archives-XLII-2-W3-187-2017}, pages = {187 -- 194}, abstract = {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.}, language = {en} } @inproceedings{ChizhovaGurianovKorovinetal., author = {Chizhova, Maria and Gurianov, Andrey and Korovin, Dmitrii and Brunn, Ansgar and Stilla, Uwe}, title = {Automatically Extraction and Reconstruction of Cupola Geometries of Orthodox Churches from Precision Point Clouds}, series = {37. Wissenschaftlich-Technische Jahrestagung der DGPF in W{\"u}rzburg - Publikationen der DGPF}, booktitle = {37. Wissenschaftlich-Technische Jahrestagung der DGPF in W{\"u}rzburg - Publikationen der DGPF}, number = {26}, pages = {434 -- 443}, abstract = {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}, language = {en} } @inproceedings{ChizhovaGurianovBrunnetal., author = {Chizhova, Maria and Gurianov, Andrey and Brunn, Ansgar and Stilla, Uwe and Luhmann, Thomas}, title = {Wavelet-basierte Erkennung architektonischer Formen aus pr{\"a}zisen Punktwolken}, series = {Beitr{\"a}ge der Oldenburger 3D-Tage 2017}, booktitle = {Beitr{\"a}ge der Oldenburger 3D-Tage 2017}, editor = {Luhmann, Thomas and Schumacher, C.}, publisher = {Wichmann-Verlag}, address = {Berlin}, pages = {129 -- 141}, language = {de} }