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3D modeling of architectural objects from video data obtained with the fixed focal length lens geometry

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PL
Modelowanie 3D obiektów architektonicznych na podstawie danych wideo pozyskanych z wykorzystaniem obiektywu stało-ogniskowego
Języki publikacji
EN
Abstrakty
EN
The article describes the process of creating 3D models of architectural objects on the basis of video images, which had been acquired by a Sony NEX-VG10E fixed focal length video camera. It was assumed, that based on video and Terrestrial Laser Scanning data it is possible to develop 3D models of architectural objects. The acquisition of video data was preceded by the calibration of video camera. The process of creating 3D models from video data involves the following steps: video frames selection for the orientation process, orientation of video frames using points with known coordinates from Terrestrial Laser Scanning (TLS), generating a TIN model using automatic matching methods. The above objects have been measured with an impulse laser scanner, Leica ScanStation 2. Created 3D models of architectural objects were compared with 3D models of the same objects for which the self-calibration bundle adjustment process was performed. In this order a PhotoModeler Software was used. In order to assess the accuracy of the developed 3D models of architectural objects, points with known coordinates from Terrestrial Laser Scanning were used. To assess the accuracy a shortest distance method was used. Analysis of the accuracy showed that 3D models generated from video images differ by about 0.06 ÷ 0.13 m compared to TLS data.
PL
Artykuł zawiera opis procesu opracowania modeli 3D obiektów architektonicznych na podstawie obrazów wideo pozyskanych kamerą wideo Sony NEX-VG10E ze stałoogniskowym obiektywem. Przyjęto założenie, że na podstawie danych wideo i danych z naziemnego skaningu laserowego (NSL) możliwe jest opracowanie modeli 3D obiektów architektonicznych. Pozyskanie danych wideo zostało poprzedzone kalibracją kamery wideo. Model matematyczny kamery był oparty na rzucie perspektywicznym. Proces opracowania modeli 3D na podstawie danych wideo składał się z następujących etapów: wybór klatek wideo do procesu orientacji, orientacja klatek wideo na podstawie współrzędnych odczytanych z chmury punktów NSL, wygenerowanie modelu 3D w strukturze TIN z wykorzystaniem metod automatycznej korelacji obrazów. Opracowane modele 3D zostały porównane z modelami 3D tych samych obiektów, dla których została przeprowadzona samokalibracja metodą wiązek. W celu oceny dokładności opracowanych modeli 3D obiektów architektonicznych wykorzystano punkty naziemnego skaningu laserowego. Do oceny dokładności wykorzystano metodę najkrótszej odległości. Analiza dokładności wykazała, że dokładność modeli 3D generowanych na podstawie danych wideo wynosi około 0.06 ÷ 0.13m względem danych NSL.
Rocznik
Strony
123--138
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, Department of Photogrammetry and Remote Sensing, 2 Kaliskiego St., 00-907 Warsaw, Poland
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, Department of Photogrammetry and Remote Sensing, 2 Kaliskiego St., 00-907 Warsaw, Poland
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, Department of Photogrammetry and Remote Sensing, 2 Kaliskiego St., 00-907 Warsaw, Poland
autor
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, Department of Photogrammetry and Remote Sensing, 2 Kaliskiego St., 00-907 Warsaw, Poland
Bibliografia
  • Abdel-Aziz, Y. I. & Karara, H. M. (1971). Direct Linear Transform from comparator coordinates into object space coordinates. In Proceedings of the Symposium on Close-Range Photogrammetry, Vol. 1, January 1971, (pp: 1-18), Falls Church, Virginia, USA: American Society of Photogrammetry.
  • Ahmed, M., Dailey, M., Landabaso, J. & Herrero, N. (2010). Robust key frame extraction for 3d reconstruction from video streams. International Conference on Computer Vision Theory and Applications (VISAPP), 17-21 May 2010 (pp: 231-236). Angers, France: Springer-Verlag.
  • Barazzetti, L. & Scaioni M. (2009). Automatic orientation of image sequences for 3d object reconstruction: first results of a method integrating photogrammetric and computer vision algorithms. In Proceedings of 3D-ARCH 2009, 25-28 February 2009 (on CD-ROM). Trento, Italy: International Society of Photogrammetry and Remote Sensing (ISPRS).
  • Bauer, S., Luber, A., & Reulke, R. (2008). Evaluation of camera calibration approaches for video image detection systems. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B1), 5-11. DOI: 10.1.1.184.3536.
  • Brown, D.C., (1971). Close-range camera calibration. Photogrammetric Engineering, 37(8), 855-866. DOI: 10.1.1.14.6358.
  • Deliś, P. (2012). Integracja danych z Naziemnego Skaningu Laserowego i danych obrazowych pozyskanych kamerą wideo. [Integration of Terrestrial Laser Scanning data with image data acquired by a video camera]. Biuletyn WAT, 4/2012, 39-54.
  • Eos Systems Inc. (1992-2008). PhotoModeler Scanner 6 [computer software]. Vancouver: Eos Systems.
  • Faugeras, O., Luong, Q.-T. & Maybank, S., (1992). Camera self-calibration: theory and experiments. Computer Vision - ECCV, Lecture Notes in Computer Science, 588, (321-334). DOI: 10.1007/3-540-55426-2_37.
  • Frahm, J.M., Pollefeys, M., Clipp, B. Gallup D., Raguram R., Wu, C. & Zach., C. (2008). 3D Reconstruction of architectural scenes from uncalibrated video sequences. International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, 38(5), DOI: 10.1.1.156.3312.
  • Fulton, J.R., & Fraser, C.S. (2009). Automated reconstruction of buildings using a hand held video camera. Innovations in Remote Sensing and Photogrammetry, Lecture Notes in Geoinformation and Cartography, 393-404. DOI: 10.1007/978-3-540-93962-7_30.
  • Hansen, von W., Thonnessen, U., & Stilla, U. (2004). Detailed relief modeling of building facades from video sequences. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(3), 967-972.
  • Hao, X. & Mayer, H. (2003). Orientation and Auto-Calibration of Image Triplets and Sequences. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(3), 73-78. DOI: 10.1.1.118.1201.
  • Heikkilä, J. & Silven, O., (1997). A four-step camera calibration procedure with implicit image correction. In Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition, 17-19 June 1997 (pp. 1106-1112). San Juan, Puerto Rico: Institute of Electrical and Electronics Engineers (IEEE).
  • Hejbudzka K., Lindenbergh R., Soudarissanane S., & Humme A. (2010), Infl uence of atmospheric conditions of the range distance and number of returned points in Leica ScanStation 2 point clouds. In Proceedings of the ISPRS Commission V Mid-Term Symposium ‘Close Range Image Measurement Techniques, 21-24 June 2010 (pp. 282-287), Newcastle upon Tyne, United Kingdom: International Society of Photogrammetry and Remote Sensing (ISPRS).
  • Kędzierski, M., Wilińska, M., & Fryśkowska A. (2010). Opracowanie ortofoto obiektu zabytkowego metodami fotogrametrii bliskiego zasięgu z wykorzystaniem naziemnego skaningu laserowego. [Generation of orthophotos of historic structures using close range photogrammetry and terrestrial laser scanning]. Archiwum Fotogrametrii, Kartografi i i Teledetekcji, 21/2010, 149-158.
  • Kutulakos, K. N. & Seitz, S. M. (2000). A Theory of Shape by Space Carving. International Journal of Computer Vision, 38(3), 307-314. DOI: 10.1.1.27.3066.
  • Okutomi, M. & Kanade, T., (1991). A multiple-baseline stereo. In Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition 15(4), 3-6 June 1991 (pp: 63-69), Lahaina, Maui, Hawaii: Institute of Electrical and Electronics Engineers (IEEE).
  • Pollefeys, M., Gool, L.V., Vergauwen, M., Cornelis, K., Verbiest, F. & Tops, J. (2002). Video-to-3d, In Proceedings of Photogrammetric Computer Vision, 34(3), 9-13 September 2002 (pp. 252-258). Graz, Austria: International Archive of Photogrammetry and Remote Sensing.
  • Remondino, F., El-Hakim, S.F., Gruen, A., & Zhang L. 2008. Turning images into 3-D models. Signal Processing Magazine IEEE, 25(4), 55-65. DOI: 10.1109/MSP.2008.923093.
  • Robertson, D.P. & Cipolla R. (2009). Structure from Motion. In Varga. M. Practical Image Processing and Computer Vision (pp. 1-49). Wiley 1 edition.
  • Seo, J., Kim, S., Jho, C., & Hong, H. (2003). 3D estimation and keyframe selection for machmove. In Proceedings International Technical Conference on Circuits/Systems, 7-9 July 2003, Bokwang Phoenix Park, Korea: ITC-CSCC.
  • Thormählen, T., Broszio, H. & Weissenfeld, A., (2004). Keyframe selection for camera motion and structure estimation from multiple views. In Proceedings of the European Conference on Computer Vision, Lecture Notes in Computer Science, Vol. 3021, 11-14 May 2004 (pp: 523-535). Prague, Czech Republic: Springer.
  • Tian, Y., Gerke, M., Vosselman, G. & Zhu, Q. (2010). Knowledge-based building reconstruction from terrestrial video sequences, ISPRS Journal of Photogrammetry and Remote Sensing, 65(4), 395-408. DOI: 10.1016/j.isprsjprs.2010.05.001.
  • Topcon Corporation (2007-2008), Topcon Image Master [computer software]. Livermore: Topcon.
  • Topcon Corporation (2007-2008), Topcon Image Master Operation Manual, 2007.
  • Torr, P., Fitzgibbon, A., & Zisserman, A. (1998). Maintaining multiple motion model hypotheses through many views to recover matching and structure. In Proceedings Sixth International Conference on Computer Vision, 4-7 January 1998 (pp:485-491). Bombay, India: Computer Vision.
  • Zhang, Z. (2000). A fl exible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(11), 1330-1334. DOI: 10.1.1.145.7481.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-840a0090-88f7-4890-9986-480689e1e0fe
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