TY - JOUR A1 - Chizhova, Maria A1 - Gurianov, Andrey A1 - Hess, Mona A1 - Luhmann, Thomas A1 - Brunn, Ansgar A1 - Stilla, Uwe T1 - Semantic segmentation of building elements using point cloud hashing JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences N2 - 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). KW - semantic segmentation KW - hashing KW - Orthodox church KW - point clouds Y1 - 1018 U6 - https://doi.org/https://doi.org/10.5194/isprs-archives-XLII-2-241-2018 VL - VLII-2 SP - 241 EP - 250 ER - TY - JOUR A1 - Chizhova, Maria A1 - Korovin, Dmitriyi A1 - Gurianov, Andrey A1 - Brodovskii, Maxim A1 - Brunn, Ansgar A1 - Stilla, Uwe A1 - Luhmann, Thomas T1 - Probabilistic Reconstruction of orthodox Churches from precision Point Clouds using Bayesian Networks and Cellular Automata JF - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences N2 - 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. KW - reconstruction from point cloud KW - cellular automaton KW - Bayesian network KW - orthodox churches Y1 - 2017 UR - https://www.pf.bgu.tum.de/pub/2017/chizhova_co_stilla_3dvrvca_pap.pdf U6 - https://doi.org/doi:10.5194/isprs-archives-XLII-2-W3-187-2017 VL - VLII-2/W3 SP - 187 EP - 194 ER - TY - CHAP A1 - Chizhova, Maria A1 - Gurianov, Andrey A1 - Brunn, Ansgar A1 - Stilla, Uwe A1 - Luhmann, Thomas ED - Luhmann, Thomas ED - Schumacher, C. T1 - Wavelet-basierte Erkennung architektonischer Formen aus präzisen Punktwolken T2 - Beiträge der Oldenburger 3D-Tage 2017 Y1 - 2017 SP - 129 EP - 141 PB - Wichmann-Verlag CY - Berlin ER -