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Orientierung großer Bildverbände

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Handbuch der Geodäsie

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Zusammenfassung

Dieses Kapitel beschäftigt sich mit der Orientierung großer Bildverbände, wie sie durch die Kombination von Bodenaufnahmen und Bildern von kleinen Drohnen – UASs (Unmanned Aircraft Systems) sowie Luftbildern entstehen. Die Bilder können sich hierbei in beliebiger Art und Weise überlappen und bilden einen Graphen. Die Orientierung ist schwierig, weil die Bilder mit großer Basis, d.h. aus sehr verschiedenen Blickwinkeln auf das Objekt und / oder aus unterschiedlichem Abstand, aufgenommen sein können. Weiterhin können Aufnahmen zu sehr unterschiedlichen Zeiten und damit mit sehr unterschiedlicher Beleuchtung erfolgt sein. Das Kapitel gibt zunächst einen überblick über die Entwicklungen im Bereich der Orientierung und beschreibt dann einen selbst entwickelten Ansatz. Ein Schwerpunkt liegt auf sehr robusten Verfahren, insbesondere RANSAC, und direkten Lösungen, vor allem dem 5-Punkt Algorithmus. Zur Reduktion der Komplexität führt die Orientierung über Paare und Triplets von Bildern zu immer größeren Bildverbänden. Weiterhin wird dargestellt, wie die Verknüpfung der Bilder vollautomatisch bestimmt werden kann. Ergebnisse für die Bildorientierung zeigen die Leistungsfähigkeit des vorgestellten Ansatzes auf.

Dieser Beitrag ist Teil des Handbuchs der Geodäsie, Band „Photogrammetrie und Fernerkundung“, herausgegeben von Christian Heipke, Hannover.

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Literatur

  1. Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a day. In: Twelfth International Conference on Computer Vision, Kyoto, S. 72–79 (2009)

    Google Scholar 

  2. Bartelsen, J., Mayer, H.: Orientation of image sequences acquired from UAVs and with GPS cameras. Surv. Land Inf. Sci. 70(3), 151–159 (2010)

    Google Scholar 

  3. Bartelsen, J., Mayer, H., Hirschmüller, H., Kuhn, A., Michelini, M.: Orientation and dense reconstruction from unordered wide baseline image sets. Photogrammetrie – Fernerkundung – Geoinformation 4/12, 421–432 (2012)

    Google Scholar 

  4. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2006)

    Google Scholar 

  5. Beder, C., Steffen, R.: Determining an initial image pair for fixing the scale of a 3D reconstruction from an image sequence. In: 28th Conference on Pattern Recognition, Deutsche Arbeitsgemeinschaft für Mustererkennung, S. 657–666. Springer, Berlin/Heidelberg (2006)

    Google Scholar 

  6. Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Conference on Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition ’97, S. 1000. IEEE Computer Society, Washington, DC (1997)

    Google Scholar 

  7. Bellman, R., Bellman, R.: Dynamic Programming. Rand Corporation. Princeton University Press, Princeton (1957)

    Google Scholar 

  8. Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Pattern Recognition – DAGM 2003, S. 249–256. Springer, Berlin (2003)

    Google Scholar 

  9. Drake, D.E., Hougardy, S.: On approximation algorithms for the terminal Steiner tree problem. Inf. Process. Lett. 89(1), 15–18 (2004)

    Article  Google Scholar 

  10. Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with Applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  Google Scholar 

  11. Fitzgibbon, A., Zisserman, A.: Automatic camera recovery for closed image sequences. In: Fifth European Conference on Computer Vision, Freiburg, 1. Aufl. I, S. 311–326 (1998)

    Google Scholar 

  12. Förstner, W.: On the geometric precision of digital correlation. Int. Arch. Photogramm. Remote Sens. 24(3), 176–189 (1982)

    Google Scholar 

  13. Förstner, W.: Quality assessment of object location and point transfer using digital image correlation techniques. Int. Arch. Photogramm. Remote Sens. 25(A3), 197–219 (1984)

    Google Scholar 

  14. Förstner, W., Gülch, E.: A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, S. 281–305 (1987)

    Google Scholar 

  15. Förstner, W., Dickscheid, T., Schindler, F.: Detecting interpretable and accurate scale-invariant keypoints. In: Twelfth International Conference on Computer Vision, Kyoto, S. 2256–2263 (2009)

    Google Scholar 

  16. Frahm, J.M., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a cloudless day. In: Eleventh European Conference on Computer Vision, Heraklion, Bd. IV, S. 368–381 (2010)

    Google Scholar 

  17. Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)

    Article  Google Scholar 

  18. Fukunaga, K., Narendra, P.M.: A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Comput. C-24(7), 750–753 (1975)

    Article  Google Scholar 

  19. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: 25th International Conference on Very Large Data Bases, Very Large Data Bases ’99, S. 518–529. Morgan Kaufmann, Orlando (1999)

    Google Scholar 

  20. Goesele, M., Ackermann, J., Fuhrmann, S., Klowsky, R., Langguth, F., Muecke, P., Ritz, M.: Scene reconstruction from community photo collections. IEEE Comput. 43(6), 48–53 (2010)

    Article  Google Scholar 

  21. Grün, A.: Adaptive least squares correlation: a powerful image matching technique. S. Afr. J. Photogramm. Remote Sens. Cartogr. 14(3), 175–187 (1985)

    Google Scholar 

  22. Guohui Lin, G.X.: On the terminal Steiner problem. Inf. Process. Lett. 84(2), 103–107 (2002)

    Google Scholar 

  23. Hakimi, S.L.: Steiner’s problem in graphs and its implications. Networks 1(2), 113–133 (1971)

    Article  Google Scholar 

  24. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Conference, S. 147–152 (1988)

    Google Scholar 

  25. Hartley, R.: Lines and points in three views and the trifocal tensor. Int. J. Comput. Vis. 22(2), 125–140 (1997)

    Article  Google Scholar 

  26. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  27. Havlena, M., Schindler, K.: VocMatch: efficient multiview correspondence for structure from motion. In: Thirteenth European Conference on Computer Visio, Zurich, 3. Aufl. S. 46–60 (2014)

    Google Scholar 

  28. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Thirtieth Annual ACM Symposium on Theory of Computing, Symposium on Theory of Computing ’98, S. 604–613. ACM, New York (1998)

    Google Scholar 

  29. Jeong, Y., Nistér, D., Steedly, D., Szeliski, R., Kweon, I.S.: Pushing the envelope of modern methods for bundle adjustment. IEEE Trans Pattern Anal Mach Intell 34(8), 1605–1617 (2012)

    Article  Google Scholar 

  30. Jian, Y.D., Balcan, D., Dellaert, F.: Generalized subgraph preconditioners for large-scale bundle adjustment. In: Thirteenth International Conference on Computer Vision, Barcelona, S. 295–302 (2011)

    Google Scholar 

  31. Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition ’04, S. 506–513. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  32. Klingner, B., Martin, D., Roseborough, J.: Street view motion-from-structure-from-motion. In: Fourteenth International Conference on Computer Vision, Sydney, S. 953–960 (2013)

    Google Scholar 

  33. Klopschitz, M., Irschara, A., Reitmayr, G., Schmalstieg, D.: Robust incremental structure from motion. In: Fifth International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), Paris, S. 1–7 (2010)

    Google Scholar 

  34. Kou, L., Markowsky, G., Berman, L.: A fast algorithm for Steiner trees. Acta Informatica 15(2), 141–145 (1981)

    Article  Google Scholar 

  35. Kuhn, A., Hirschmüller, H., Mayer, H.: Multi-resolution range data fusion for multi-view stereo reconstruction. In: German Conference on Pattern Recognition – GCPR 2013, S. 41–50. Springer, Berlin (2013)

    Google Scholar 

  36. Kuhn, A., Mayer, H., Hirschmüller, H., Scharstein, D.: A TV prior for high quality local multi-view stereo reconstruction. In: 2nd International Conference on 3D Vision (3DV), Tokyo, S. 65–72 (2014)

    Google Scholar 

  37. Leibe, B., Mikolajczyk, K., Schiele, B.: Efficient clustering and matching for object class recognition. In: Proceedings of British Machine Vision Conference, Edinburgh (2006)

    Book  Google Scholar 

  38. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  39. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop, Washington, DC, S. 121–130 (1981)

    Google Scholar 

  40. Mayer, H.: Robust orientation, calibration, and disparity estimation of image triplets. In: Pattern Recognition – DAGM 2003, S. 281–288. Springer, Berlin (2003)

    Google Scholar 

  41. Mayer, H.: Robust least-squares adjustment based orientation and auto-calibration of wide-baseline image sequences. In: ISPRS Workshop in Conjunction with ICCV 2005 “Towards Benchmarking Automated Calibration, Orientation and Surface Reconstruction from Images” (BenCos), Beijing, 1–6 (2005)

    Google Scholar 

  42. Mayer, H.: 3D reconstruction and visualization of urban scenes from uncalibrated wide-baseline image sequences. Photogrammetrie – Fernerkundung – Geoinformation 3/07, 167–176 (2007)

    Google Scholar 

  43. Mayer, H.: Issues for image matching in structure from motion. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37(B3a), 21–26 (2008)

    Google Scholar 

  44. Mayer, H.: Efficient hierarchical Triplet merging for camera pose estimation. In: German Conference on Pattern Recognition – GCPR 2014, S. 399–409. Springer, Berlin (2014)

    Google Scholar 

  45. Mayer, H., Reznik, S.: Building facade interpretation from uncalibrated wide-baseline image sequences. ISPRS J. Photogramm. Remote Sens. 61(6), 371–380 (2007)

    Article  Google Scholar 

  46. Mayer, H., Bartelsen, J., Hirschmüller, H., Kuhn, A.: Dense 3D reconstruction from wide baseline image sets. In: Real-World Scene Analysis 2011. Lecture Notes in Computer Science, S. 285–304. Springer, Berlin (2012)

    Google Scholar 

  47. McGlone, J. (Hrsg.): Manual of Photogrammetry, 6. Aufl. American Society of Photogrammetry and Remote Sensing, Bethesda (2013)

    Google Scholar 

  48. Mehlhorn, K.: A faster approximation algorithm for the Steiner problem in graphs. Inf. Process. Lett. 27(3), 125–128 (1988)

    Article  Google Scholar 

  49. Michelini, M., Mayer, H.: Detection of critical camera configurations for structure from motion. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XL-3/W1, 73–78 (2014)

    Google Scholar 

  50. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  51. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., van Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)

    Article  Google Scholar 

  52. Morel, J.M., Yu, G.: ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)

    Article  Google Scholar 

  53. Muja, M., Lowe, D.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Applications, Lisboa, S. 331–340 (2009)

    Google Scholar 

  54. Nguatem, W., Drauschke, M., Mayer, H.: Localization of windows and doors in 3D point clouds of Facades. Int. Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II-3, 87–94 (2014)

    Article  Google Scholar 

  55. Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–770 (2004)

    Article  Google Scholar 

  56. Nistér, D.: Untwisting a projective reconstruction. Int. J. Comput. Vis. 60(2), 165–183 (2004)

    Article  Google Scholar 

  57. Nistér, D., Schaffalitzky, F.: Four points in two or three calibrated views: theory and practice. Int. J. Comput. Vis. 67(2), 211–231 (2006)

    Article  Google Scholar 

  58. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition – Computer Vision and Pattern Recognition ’06, 2. Aufl. S. 2161–2168. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  59. Pollefeys, M., Van Gool, L.: Stratified self-calibration with the modulus constraint. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 707–724 (1999)

    Google Scholar 

  60. Pollefeys, M., Verbiest, F., Van Gool, L.: Surviving dominant planes in uncalibrated structure and motion recovery. In: Seventh European Conference on Computer Vision, Copenhagen, 2. Aufl. S. 837–851 (2002)

    Google Scholar 

  61. Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J.: Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)

    Google Scholar 

  62. Pollefeys, M., Nistér, D., Frahm, J.M., Akbarzadeh, A., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Kim, S.J., Merrell, P., Salmi, C., Sinha, S., Talton, B., Wang, L., Yang, Q., Stewénius, H., Yang, R., Welch, G., Towles, H.: Detailed real-time urban 3D reconstruction from video. Int. J. Comput. Vis. 78(2–3), 143–167 (2008)

    Article  Google Scholar 

  63. Reznik, S., Mayer, H.: Implicit shape models, self diagnosis, and model selection for 3D facade interpretation. Photogrammetrie – Fernerkundung – Geoinformation 3/08, 187–196 (2008)

    Google Scholar 

  64. Strecha, C., von Hansen, W., Van Gool, L., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: Computer Vision and Pattern Recognition, Anchorage, S. 1–8 (2008)

    Google Scholar 

  65. Strecha, C., Pylvänäinen, T., Fua, P.: Dynamic and scalable large scale image reconstruction. In: Computer Vision and Pattern Recognition, San Francisco, S. 358–365 (2010)

    Google Scholar 

  66. Torr, P.: An assessment of information criteria for motion model selection. In: Computer Vision and Pattern Recognition, San Juan, S. 47–53 (1997)

    Google Scholar 

  67. Torr, P., Zisserman, A.: Robust parametrization and computation of the trifocal tensor. Image Vis. Comput. 15, 591–605 (1997)

    Article  Google Scholar 

  68. Wu, C.: SiftGPU: A GPU implementation of scale invariant feature transform (SIFT) (2007). http://cs.unc.edu/~ccwu/siftgpu. Zugegriffen am 02.03.2016

  69. Zelikovsky, A.: An 11/6-approximation algorithm for the network Steiner problem. Algorithmica 9(5), 463–470 (1993)

    Article  Google Scholar 

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  1. Institut für Angewandte Informatik, Universität der Bundeswehr München, Neubiberg, Deutschland

    Helmut Mayer & Mario Michelini

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  1. Helmut Mayer
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  2. Mario Michelini
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  1. Lehr- und Forschungsgebiet "Geomathemati, TU Kaiserslautern, Kaiserslautern, Germany

    Willi Freeden

  2. Physikalische Geodäsie, TU München Inst. Astronomische und, München, Germany

    Reiner Rummel

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Mayer, H., Michelini, M. (2015). Orientierung großer Bildverbände. In: Freeden, W., Rummel, R. (eds) Handbuch der Geodäsie. Springer Reference Naturwissenschaften . Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46900-2_39-1

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