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Problemy automatycznego modelowania i teksturowania obiektów opisujących skrajnię linii kolejowych

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Warianty tytułu
EN
Problems of automatic modelling and texturing of objects that describe railway line clearance gauge
Języki publikacji
PL
Abstrakty
PL
W artykule podjęty jest problem automatycznego modelowania i teksturowania obiektów kształtujących skrajnię kolejową. Źródłem modelowania jest mobilny skaning laserowy, a teksturowania – zdjęcia wykonane podczas skaningu. Artykuł składa się z dwóch części: przeglądu literatury i opisu eksperymentu badawczego. Eksperyment badawczy potwierdził większość problemów artykułowanych w literaturze, wniósł także szereg nowych. Przestrzeń obiektów przytorowych była bardzo trudnym materiałem do wymodelowania i pokrycia teksturą fotograficzną, co uwypukliło problemy. W konkluzji wskazano, że główną przeszkodą w automatyzacji procesu teksturowania jest słaba jakość modeli generowanych automatycznie.
EN
The goal of the paper is to present results of research that has been conducted for several years in the Department of Geoinformatics, Photogrammetry and Environmental Remote Sensing, AGH University of Science and Technology in the field of automatic modelling and texturing of objects that describe railway line clearance gauges. In the paper, the authors have presented bases of 3D modelling and texturing of objects, with particular consideration of railway clearance gauge objects. An attempt has been made at assessing possibilities of automatic and semi-automatic reconstruction of objects located in the direct vicinity of railway tracks on the basis of a point cloud from a mobile scanning obtained, together with digital images from four cameras, for the selected test railway line section on Cracow-Warsaw route. Railway objects shape construction gauge of railway lines, therefore virtual geometric model of those objects might be used for checking if railway rolling stock elements of specified dimensions can safely fit the gauge outline. A couple of programs have been tested in the framework of research (PhotoModeler Scanner, RiScan Pro, 3DReshaper) as regards their usability for modelling and texturing. In research works a previously prepared triangle model has been utilized, as well as a set of 160 images made with the use of four cameras. Additional data included camera and distortion parameters, and elements of external orientation of obtained images. The paper shows exemplary results and indicates problems originating in the course of creating models by means of point cloud vectorization. The authors' experiences show that this is a far more difficult process than stereoscopic photogrammetric model vectorization. Only an experienced operator, with a good spatial imagination, is capable of producing a correct model. That is why in majority of solutions, semi-automatic methods are applied. Those methods consist in the operator's determining of a type (or, possibly, an approximated shape) of the object to be detected, as well as its rough location, and it is the task of the algorithm to fit the object into a set area. The conducted research has demonstrated that there is no thoroughly satisfactory method (program) for automatic modelling and texturing of railway line clearance gauge. As of today, numerous projects have still to be performed in either a semi-automatic, or a manual way.
Rocznik
Tom
Strony
177--188
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
  • Katedra Geoinformacji, Fotogrametrii i Teledetekcji Środowiska, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
autor
  • Katedra Geoinformacji, Fotogrametrii i Teledetekcji Środowiska, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
autor
  • Katedra Geoinformacji, Fotogrametrii i Teledetekcji Środowiska, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
autor
  • Katedra Geoinformacji, Fotogrametrii i Teledetekcji Środowiska, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
autor
  • Katedra Geoinformacji, Fotogrametrii i Teledetekcji Środowiska, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
Bibliografia
  • 1. Abdul-Rahman A., Pilouk M., 2007. Spatial Data Modelling for 3D GIS. Springer, Berlin, Heidelberg, New York.
  • 2. Alshawabkeh, Y., Haala, N., 2004. Integration of digital photogrammetry and laser scanning for heritage documentation. In: International Archives of Photogrammetry and Remote Sensing, XXth ISPRS Congress. Istanbul, Turkey: ISPRS.
  • 3. Briese C., Pfeifer N., 2008. Towards automatic feature Line model ling from terrestrial laser Scanner data. International Archives of Photogrammetry and Remote Sensing XXIst ISPRS Congress: Commission V, WG 3, Beijing.
  • 4. Debevec P., Taylor C.J., Malik G., 1996. Modeling and rendering architecture from photographs: a hybrid geometry - and image - based approach. Computer Graphics, (SIGGRAPH ’96 Proceedings).
  • 5. El-Hakim S.,Gonzo L., Picard M., Girardi S., Simoni A., 2003. Visualization of frescoed surfaces: Buonconsiglio Castle - Aquila Tower. Int. Workshop on Visualization & Animation of Reality - Based 3D Models, Tarasp-Vulpera, Switzerland.
  • 6. Foley J.D., Dam A.van., Feiner S.K., Hughes J.F., Philips R.L., 2001. Wprowadzenie do grafiki komputerowej. Warszawa Wydawnictwa Naukowo-Techniczne, Warszawa.
  • 7. Grammatikopoulos L., Kalisperakis L., Karras G., Kokkinos T., Petsa E., 2004. On automatic orthoprojection and texture-mapping of 3D surface models, Int. Arch. Photogrammetry, Remote, Sensing & Spatial Information Sciences, 35(5).
  • 8. Gumhold S., Wang X., MacLeod R., 2001. Feature extraction from point Cloud. Proc. 10th International Meshing Roundtable.
  • 9. Hanusch, T., 2008. A new texture mapping algorithm for photorealistic reconstruction of 3D object. Proceedings of the XXI-th ISPRS Congress, Beijing, China.
  • 10. Hoppe C., Krömker S., 2009. Adaptive meshing and detail-reduction of 3D-point clouds from laser scans. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , Volume XXXVIII-5/W1.
  • 11. Huang J., Menq C-H., 2001. Automatic data segmentation for geometric feature extraction from unorganized 3-d coordinate points. IEEE Transactions on Robotics and Automation.
  • 12. Lai K., Fox D., 2010. Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation. The International Journal of Robotics Research.
  • 13. Lehtomäki M., Jaakkola A., Hyyppa J., Kukko A., Kaartinen H., 2010. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sensing 2 (3).
  • 14. Mehdi-Souzani C., Digne J., Audfray N., Lartigue C., Morel J.-M., 2010. Feature extraction from high- density point clouds: toward automation of an intelligent 3D contactless digitizing strategy. Computer-Aided Design and Applications ,Vol. 7/6.
  • 15. Neugebauer P., Klein K., 1999. Texturing 3D Models of Real World Objects From Multiple unregistered photographic view. Proc. Eurographics '99, Computer Graphics Forum, 1999, 18(3).
  • 16. Pauly M., Keiser R., Gross M., 2003. Multi-scale feature extraction on point-sampled surfaces. Computer Graphics Forum 22.
  • 17. Tarsha-Kurdi F., Landes T., Grussenmeyer P., Koehl M., 2007. Model-Driven and Data- Driven Approaches Using LIDAR Data: Analysis and Comparison. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVI-3/W49A.
  • 18. Weber C., Hahmann S., Hagen H., 2010a. Methods for Feature Detection In Point Clouds. Shape Modeling International Conference (SMI).
  • 19. Weber C., Hahmann S., Hagen H., 2010b. Sharp feature detection in point clouds. Proceedings SMI ’10.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2c24a122-3094-454a-88da-1f0e74fb3d22
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