@inproceedings{ChizhovaBrunn, author = {Chizhova, Maria and Brunn, Ansgar}, title = {Aufbau wissenschaftlicher Partnerschaften nach Osteuropa - ein aktueller Erfahrungsbericht}, series = {DGPF-Tagung, K{\"o}ln, 2015}, booktitle = {DGPF-Tagung, K{\"o}ln, 2015}, abstract = {Der Aufbau internationaler Partnerschaften nach Osteuropa ist aktuell ein Thema f{\"u}r viele Hochschulen im deutschsprachigen Raum. Gerade die Studieng{\"a}nge der Vermessung und Geoinformatik haben oftmals Schwierigkeiten Partner im Ausland zu finden, da das Fach in vielen L{\"a}ndern nicht in der gleichen Form gelehrt wird. Dieser Beitrag zeigt aktuelle M{\"o}glichkeiten des Aufbaus von Partnerschaften nach Russland anhand der Erfahrungen der Hochschule f{\"u}r angewandte Wissenschaften W{\"u}rzburg-Schweinfurt auf. Dazu werden die aktuelle fachspezifische Situation in der Hochschulausbildung wie auch die Struktur der Ausbildung in Russland vorgestellt, kurz mit der Situation an der Hochschule f{\"u}r angewandte Wissenschaften W{\"u}rzburg-Schweinfurt verglichen sowie die M{\"o}glichkeiten der Zusammenarbeit aufgezeigt.}, language = {de} } @article{ChizhovaBrunn, author = {Chizhova, Maria and Brunn, Ansgar}, title = {ОТ ФОТОСНИМКА К ОБЛАКУ ТОЧЕК: СОЗДАНИЕ 3D-МОДЕЛЕЙ НА ОСНОВЕ ОБЛАКА ТОЧЕК, ПОЛУЧЕННЫХ ФОТОГРАММЕТРИЧЕСКИМ СПОСОБОМ}, series = {Sammelband der Akademie Iwanowo 2015}, journal = {Sammelband der Akademie Iwanowo 2015}, language = {ru} } @article{ChizhovaBrunnStilla, author = {Chizhova, Maria and Brunn, Ansgar and Stilla, Uwe}, title = {Probabilistic feasibility of the HE reconstruction process of russian-orthodox churches}, series = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {XLI-B5}, journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {https://doi.org/10.5194/isprs-archives-XLI-B5-221-2016}, pages = {221 -- 227}, abstract = {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.}, language = {en} } @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} } @incollection{BrunnChizhova, author = {Brunn, Ansgar and Chizhova, Maria}, title = {Aufbau wissenschaftlicher Partnerschaften nach Osteuropa}, series = {DGPF-Tagung}, booktitle = {DGPF-Tagung}, address = {K{\"o}ln}, language = {de} } @article{BrodovskiiKorovinChizhovaetal., author = {Brodovskii, Maksim and Korovin, Dmitrii and Chizhova, Maria and Brunn, Ansgar and Stilla, Uwe}, title = {Recognizing Separate Structural Elements of Churches Using Neural Network}, series = {Dreil{\"a}ndertagung der DGPF, der OVG und der SGPF in Bern, Schweiz - Publikationen der DGPF}, journal = {Dreil{\"a}ndertagung der DGPF, der OVG und der SGPF in Bern, Schweiz - Publikationen der DGPF}, abstract = {In this paper we develop a new approach to recognize structural elements of orthodox churches. We will work with 3D point clouds, received as a result of 3D point cloud acquisitions of churches, e.g. from laser scanning. Because of the large amount of points in such clouds, we have to use a projection (elevation) to decrease the calculation effort. To get meaningful images from the projection of the point cloud we do some prior segmentation of the 3D cloud. Images binary, with a predefined resolution that depends on the resolution of the 3D point cloud. To recognize elements we decide to use neural networks (Perceptron and Counter propagation neural networks) as they allow the automation of the process and have a broad range of methods to recognize images. For the subsequent 3D modeling we use analytic expressions, that describe each of the structural church elements. A further step is the deduction of those expressions that describe each of the recognized sectional views}, language = {en} }