Abstract
In this paper, we address the problem of the recovery of the Euclidean geometry of a scene from a sequence of images without any prior knowledge either about the parameters of the cameras, or about the motion of the cam-era(s). We do not require any knowledge of the absolute coordinates of some control points in the scene to achieve this goal. Using various computer vision tools, we establish correspondences between images and recover the epipolar geometry of the set of images, from which we show how to compute the complete set of perspective projection matrices for each camera position. These being known, we proceed to reconstruct the scene. This reconstruction is defined up to an unknown projective transformation (i.e. is parameterized with 15 arbitrary parameters). Next we show how to go from this reconstruction to a more constrained class of reconstructions, defined up to an unknown affine transformation (i.e. parameterized with 12 arbitrary parameters) by exploiting known geometric relations between features in the scene such as parallelism. Finally, we show how to go from this reconstruction to another class, defined up to an unknown similitude (i.e. parameterized with 7 arbitrary parameters). This means that in an Euclidean frame attached to the scene or to one of the cameras, the reconstruction depends only upon one parameter, the global scale. This parameter is easily fixed as soon as one absolute length measurement is known. We see this vision system as a building block, a vision server, of a CAD system that is used by a human to model a scene for such applications as simulation, virtual or augmented reality. We believe that such a system can save a lot of tedious work to the human observer as well as play a leading role in keeping the geometric data base accurate and coherent.
Stéphane Laveau is supported by a grant under DRET contract No 91–815/DRET/EAR. This work was also partially funded by the EEC under Esprit, project 6448, Viva and Esprit, project. 8878, Realise.
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References
Barrett, E. B., Brill, M. H., Haag, N. N., and Payton, P. M. (1992). Invariant Linear Methods in Photogrammetry and Model-Matching. In Mundy, J. L. and Zimmerman, A., editors, Geometric Invariance in Computer Vision, chapter 14. MIT Press.
Blaszka, T. and Deriche, R. (1994). Recovering and characterizing image features using an efficient model based approach. Technical Report 2422, INRIA.
Boufama, B. and Mohr, R. (1995). Epipole and fundamental matrix estimation using the virtual parallax property. In Proceedings of the International Conference on Computer Vision, Cambridge, Ma. IEEE Computer Society Press. To appear.
Brown, D. C. (1958). A solution to the general problem of multiple station analytical stereotriangulation. Technical Report 43, RCA Data Reduction Technical Report, Patrick Air Force base, Florida.
Brown, D. C. (1971). Close-Range Camera Calibration. Photogram-metric Engineering, 37(8):855–866.
Csurka, G., Zeller, C., and Zhang, Zhengyouand, Faugeras, O. (1995). Characterizing the uncertainty of the fundamental matrix. Technical report, INRIA.
Faugeras, O. (1992). What can be seen in three dimensions with an uncalibrated stereo rig. In Sandini, G., editor, Proceedings of the 2nd European Conference on Computer Vision, volume 588 of Lecture Notes in Computer Science, pages 563–578, Santa Margherita Ligure, Italy. Springer-Verlag.
Faugeras, O. (1993). Three-Dimensional Computer Vision: a Geometric Viewpoint. The MIT Press.
Faugeras, O. (1995). Stratification of 3-d vision: projective, affine, and metric representations. Journal of the Optical Society of America A, 12(3):465–484.
Faugeras, O. and Laveau, S. (1994). Representing three-dimensional data as a collection of images and fundamental matrices for image synthesis. In Proceedings of the International Conference on Pattern Recognition, pages 689–691, Jerusalem, Israel. Computer Society Press.
Faugeras, O., Luong, T., and Maybank, S. (1992). Camera self-calibration: theory and experiments. In Sandini, G., editor, Proc. Second European Conference on Computer Vision, number 588 in Lecture Notes in Computer Science, pages 321–334, Santa-Margherita, Italy. Springer-Verlag.
Faugeras, O. and Mourrain, B. (1995a). On the geometry and algebra of the point and line correspondences between n images. In Proceedings of the International Conference on Computer Vision, Cambridge, Ma. IEEE Computer Society Press. To appear.
Faugeras, O. and Mourrain, B. (1995b). On the geometry and algebra of the point and line correspondences between n images. Technical report, INRIA.
Förstner, M. A. and Gtilch, E. (1987). A fast operator for detection and precise location of distinct points, corners and centers of circular features. In Proceedings of the Intercommission Workshop of the International Society for Photogrammetry and Remote Sensing, Interlaken, Switzerland.
Gruen, A. (1978). Accuracy, reliability and statistics in close-range photogrammetry. In Proceedings of the Symposium of the ISP Commission V, Stockholm.
Gruen, A. and Beyer, H. A. (1992). System calibration through self-calibration. In Proceedings of the Workshop on Calibration and Orientation of Cameras in Computer Vision, Washington D.C.
Guiducci, A. (1988). Corner characterization by differential geometry techniques. Pattern Recognition Letters, 8:311–318.
Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Proceedings Alvey Conference, pages 189–192.
Hartley, R. (1993). Euclidean reconstruction from uncalibrated views. In Mundy, J. and Zisserman, A., editors, Applications of Invariance in Computer Vision, volume 825 of Lecture Notes in Computer Science, pages 237–256, Berlin. Springer-Verlag.
Hartley, R., Gupta, R., and Chang, T. (1992). Stereo from uncalibrated cameras. In Proc. International Conference on Computer Vision and Pattern Recognition, pages 761–764, Champaign, Illinois. IEEE.
Hartley, R. I. (1992). Estimation of relative camera positions for uncalibrated cameras. In European Conference on Computer Vision, pages 579–587, Santa Margherita Ligure, Italy. Springer-Verlag.
Kitchen, L. and Rosenfeld, A. (1982). Gray-level corner detection. Pattern Recognition Letters, pages 95–102.
Koenderink, J. J. and van Doom, A. J. (1991). Affine Structure from Motion. Journal of the Optical Society of America, A8:377–385.
Laveau, S. and Faugeras, O. (1994). 3-D scene representation as a collection of images andfundamental matrices. Technical Report 2205, INRIA.
Luong, Q.-T. (1992). Matrice Fondamentale et Calibration Visuelle sur VEnvironnement-Vers une plus grande autonomie des systèmes robotiques. PhD thesis, Université de Paris-Sud, Centre d’Orsay.
Luong, Q.-T., Deriche, R., Faugeras, O., and Papadopoulo, T. (1993a). On determining the fundamental matrix: Analysis of different methods and experimental results. Technical Report 1894, INRIA.
Luong, Q. T., Deriche, R., Faugeras, O. D., and Papadopoulo, T. (1993b). On determining the Fundamental matrix: analysis of different methods and experimental results. In Israelian Conf on Artificial Intelligence and Computer Vision, Tel-Aviv, Israel. A longer version is INRIA Tech Report RR-1894.
Luong, Q.-T. and Faugeras, O. (1994a). Camera Calibration, Scene Motion and Structure recovery from point correspondences and Fundamental matrices. The International Journal of Computer Vision. Submitted.
Luong, Q.-T. and Faugeras, O. (1994b). The fundamental matrix: theory, algorithms, and stability analysis. The International Journal of Computer Vision. To appear.
Luong, Q.-T. and Viéville, T. (1994). Canonic representations for the geometries of multiple projective views. In Eklundh, J.-O., editor, Proceedings of the 3rd European Conference on Computer Vision, pages 589–599, Vol. I. Springer-Verlag, Lecture Notes in Computer Science 800–801.
Luong, T. and Faugeras, O. (1994c). A stability analysis of the fundamental matrix. In Eklundh, J.-O., editor, Proceedings of the 3rd European Conference on Computer Vision, pages 577–588, Stockholm, Sweden. Springer-Verlag.
Maybank, S. J. and Faugeras, O. D. (1992). A theory of self-calibration of a moving camera. The International Journal of Computer Vision, 8(2):123–152.
Mundy, J. L. and Zisserman, A., editors (1992). Geometric Invariance in Computer Vision. MIT Press.
Noble, J. (1988). Finding corners. Image and Vision Computing, 6:121–128.
Olsen, S. (1992). Epipolar line estimation. In Proc. Second European Conference on Computer Vision, pages 307–311, Santa Margherita Ligure, Italy.
Rothwell, C, Csurka, G., and Faugeras, O. (1995). A comparison of projective reconstruction methods for pairs of views. In Proceedings of the International Conference on Computer Vision, Cambridge, Ma. IEEE Computer Society Press. To appear.
Rousseeuw, P. and Leroy, A. (1987). Robust Regression and Outlier Detection. John Wiley & Sons, New York.
Semple, J. and Kneebone, G. (1952). Algebraic Projective Geometry. Oxford: Clarendon Press. Reprinted 1979.
Slama, C. C., editor (1980). Manual of Photogrammetry. American Society of Photogrammetry, fourth edition.
Ullman, S. and Basri, R. (1991). Recognition by Linear Combinations of Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):992–1006.
Zhang, Z., Deriche, R., Faugeras, O., and Luong, Q.-T. (1994). A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Research Report 2273, INRIA Sophia-Antipolis, France, submitted to Artificial Intelligence Journal.
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Faugeras, O., Laveau, S., Robert, L., Csurka, G., Zeller, C. (1995). 3-D Reconstruction of Urban Scenes from Sequences of Images. In: Gruen, A., Kuebler, O., Agouris, P. (eds) Automatic Extraction of Man-Made Objects from Aerial and Space Images. Monte Verità. Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-9242-1_15
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DOI: https://doi.org/10.1007/978-3-0348-9242-1_15
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