Segmentación de mallas 3D de edificios históricos para levantamiento arquitectónico

Borja Javier Herráez; Eduardo Vendrell Vidal

doi:10.4995/var.2018.5858

Authors

Borja Javier Herráez Universitat Politècnica de València https://orcid.org/0000-0001-7594-0302
Eduardo Vendrell Vidal Universitat Politècnica de València https://orcid.org/0000-0001-7945-1418

DOI:

https://doi.org/10.4995/var.2018.5858

Keywords:

segmentation, 3D meshes, graphs, pattern recognition, features

Abstract

Advances in three-dimensional (3D) acquisition systems have introduced this technology to more fields of study, such as archaeology or architecture. In the architectural field, scanning a building is one of the first possible steps from which a 3D model can be obtained and can be later used for visualisation and/or feature analysis, thanks to computer-based pattern recognition tools. The automation of these tools allows for temporal savings and has become a strong aid for professionals, so that more and more methods are developed with this objective. In this article, a method for 3D mesh segmentation focused on the representation of historic buildings is proposed. This type of buildings is characterised by having singularities and features in façades, such as doors or windows. The main objective is to recognise these features, understanding them as those parts of the model that differ from the main structure of the building. The idea is to use a recognition algorithm for planar faces that allows users to create a graph showing the connectivity between them, therefore allowing the reflection of the shape of the 3Dmodel. At a later step, this graph is matched against some pre-defined graphs that represent the patterns to look for. Each coincidence between both graphs indicate the position of one of the characteristics sought. The developed method has proved to be effective for feature detection and suitable for inclusion in architectural surveying applications.

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Author Biographies

Borja Javier Herráez, Universitat Politècnica de València

Instituto de Automática e Informática Industrial

Eduardo Vendrell Vidal, Universitat Politècnica de València

Instituto de Automática e Informática Industrial

References

Attene, M., Katz, S., Mortara, M., Patané,G., Spagnuolo,M., & Tal, A. (2006). Mesh segmentation –A comparative study. Proceedings of IEEE International Conference on Shape Modeling and Applications (pp. 14–25).doi:10.1109/SMI.2006.24

Attene,M., Falcidieno, B., &Spagnuolo, M. (2006). Hierarchical mesh segmentation based on fitting primitives. The Visual Computer, 22(3), 181–193.doi:10.1007/s00371-006-0375-x

Amazon. (2008). Mechanical turk. Retrieved from www.mturk.com.

Boykov, Y., Veksler, O., & Zabih,R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239. doi:10.1109/34.969114

Chen, X.,Golovinskiy, A., &Funkhouser, T. (2009).A benchmark for 3D mesh segmentation. ACM Transactions on Graphics (Proceedings of SIGGRAPH), 28(3),73:1–73:12.doi:10.1145/1576246.1531379

Duda, R.O., & Hart, P.E.(1972). Use of the Hough transformation to detect lines and curves in pictures.Communications of the ACM, 15(1), 11–15. doi:10.1145/361237.361242

Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM Handbook: A Guide to Building Information Modelling for Owners, Managers, Designers, Engineers and Contractors. New Jersey: John Wiley & Sons.

Fischler, M., & Bolles, R. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. doi:10.1145/358669.358692

Galler, B.A.,& Fischer, M.J.(1964).An improved equivalence algorithm. Communications of the ACM, 7(5),301–303.doi:10.1145/364099.364331

Golovinskiy, A., & Funkhouser, T. (2008). Randomized cuts for 3D mesh analysis. ACM Transactions on Graphics(Proceedings of SIGGRAPH ASIA), 27(5), 145:1–145:12.doi:10.1145/1409060.1409098

Golovinskiy, A.,& Funkhouser, T.(2009).Consistent segmentation of 3D models. Computers & Graphics (Shape Modeling International 09), 33(3),262–269.doi:10.1016/j.cag.2009.03.010

Gonzalez,R.C., & Woods,R.E. (2004).Digital image processing(2nd ed.).Upper Saddle River, New Jersey: Pearson Education.

Hopcroft, J.,& Tarjan, R. (1973). Algorithm 447: efficient algorithms for graph manipulation. Communications of the ACM16 (6),372–378. doi:10.1145/362248.362272

Katz, S., & Tal, A.(2003). Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2003), 22(3), 954–961.doi:10.1145/882262.882369

Katz, S., Leifman, G., &Tal, A. (2005). Mesh segmentation using feature point and core extraction. The Visual Computer,21, 649–658.doi:10.1007/s00371-005-0344-9

Kenneth, E.,& Lynch, M. (1995). Geographic information systems as an integrating technology: Context, concepts, and definitions (Department of Geography, University of Colorado at Boulder).Retrieved fromhttp://www.colorado.edu/geography/gcraft/notes/intro/intro.html.

Lai, Y.K., Hu, S.M., Martin, R.R., &Rosin, P.L. (2008). Fast mesh segmentation using random walks. Symposium on Solid and Physical Modeling (pp.183–191). doi:10.1145/1364901.1364927

Li.,J.,&,Kuo,C.C.(1988).A dual graph approach to 3D triangular mesh compression. Proceedings of the IEEE International Conference on Image Processing(pp. 891–894). Chicago. doi:10.1109/ICIP.1998.723699

Li, X.,Toon, T.W.,& Huang, Z.(2001).Decomposing polygon meshes for interactive applications. Symposium on Interactive 3D graphics(pp.35–42).doi:10.1145/364338.364343

Liu, R.,& Zhang, H.(2004).Segmentation of 3D meshes through spectral clustering. Pacific Conference on Computer Graphics and Applications, 298–305. doi:10.1109/PCCGA.2004.1348360

Logothetis, S., & Stylianidis, E. (2016). BIM Open Source Software (OSS) for the documentation of cultural heritage. Virtual Archaeology Review, 7(28), 28–35.doi:10.4995/var.2016.5864

Mangan, A.P.,& Whitaker, R.T. (1999). Partitioning 3D surface meshes using watershed segmentation. IEEE Transactions on Visualization and Computer Graphics, 5(4), 308–321. doi:10.1109/2945.817348

Marefat, M., & Kashyap, R. (1990).Geometric reasoning for recognition of three-dimensional object features. IEEE Transactions on Pattern Analysis and Machine Intelligence,12(10), 949–965 .doi:10.1109/34.58868

Nieto, J.E., & Moyano, J.J. (2014). The paramental study on the model of information of historic building or "HBIM Project". Virtual Archaeology Review, 5 (11), 73–85. doi:10.4995/var.2014.4183

Page, D.L.,Koschan, A., &Abidi, M. (2003). Perception-based 3D triangle mesh segmentation using fast marching watersheds. Proceedings of Computer Vision and Pattern Recognition(pp. 27–32). doi:10.1109/CVPR.2003.1211448

Shapira, L., Shamir, A., & Cohen-or, D. (2008).Consistent mesh partitioning and skeletonisation using the shape diameter function. The VisualComputer,24 (4), 249–259. doi:10.1007/s00371-007-0197-5

Sheffer,A.,(2001).Model simplification for meshing using face clustering. Computer Aided Design,33(13),925–934.doi:10.1016/S0010-4485(00)00116-0

Shlafman, S., Tal, A., &Katz, S. (2002).Metamorphosis of polyhedral surfaces using decomposition. Computer Graphics Forum, 21(3), 219–228. doi:10.1111/1467-8659.00581

Smpath, A.,& Shan, J. (2010).Segmentation and reconstruction of polyhedral building roofsfrom aerial lidar point clouds. IEEE Transactions on Geoscience and Remote Sensing, 48 (3), 1554–1567. doi:10.1109/TGRS.2009.2030180

Stamos. J.,Gene, Y.,Wolberg, G., & Zokai, S.(2006). 3D Modeling using planar segments and mesh elements.International Symposium on 3D Data Processing, Visualization, and Transmission, 599–606.doi:10.1109/3DPVT.2006.5

Tralie, C.(2010).Mesh segmentation by KMeans clustering.Retrieved fromhttp://www.ctralie.com/Teaching/MeshSeg/

Troiano, D.,García, A.,Merlo, A., &Vendrell, E. (2014). From a model of a city to an urban information system: The SIUR 3D of the castle of Pietrabuona. In M. Ioannides, N. Magnenat-Thalmann, E. Fink, R. Žarnić, A. Yen, & E. Quak (Eds.), Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2014. Lecture Notes in Computer Science, 8740. Cham: Springer. doi:10.1007/978-3-319-13695-0_12

Ullman, J.R.(1976). An algorithm for subgraph isomorphism. Journal of the ACM, 23(1), 31–42. doi:10.1145/321921.321925

Verma, V., Kumar, R., & Hsu, S. (2006).3D building detection and modelling from aerial LIDAR data. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2(pp. 2213–2220). doi:10.1109/CVPR.2006.12

Vieira, M.,& Shimada, K.,(2005). Surface mesh segmentation and smooth surface extraction through region growing. Computer-Aided Geometric Design, 22(8), 771–792. doi:10.1016/j.cagd.2005.03.006

Wan, J.,& Shan, J. (2009). Segmentation of lidar point clouds for building extraction. In ASPRS Annual Conference, Baltimore, Maryland,9–13.

Zhang, Y.,Paik, J.,Koschan, A.,& Abidi, M.A. (2002). A simple and efficient algorithm for part decomposition of 3D triangulated models based on curvature analysis. Proceedings of the IEEE International Conference on Image Processing(pp. 273–276). doi:10.1109/ICIP.2002.1038958