Abstract
In this paper we describe a mobile camera localization system that is able to accurately estimate the pose of an hand-held camera inside a known urban environment. The work leverages on a pre-computed 3D structure obtained by a hierarchical Structure from Motion pipeline to compute the 2D-3D correspondences needed to orient the camera. The hierarchical cluster structure, given by the SfM, guides the localization process providing accurate and reliable features matching. Experiments in outdoor challenging environments demonstrate the effectiveness of the method compared to a standard image retrieval approach.
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Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: SIGGRAPH: International Conference on Computer Graphics and Interactive Techniques, pp. 835–846 (2006)
Frahm, J.-M., et al.: Building rome on a cloudless day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010)
Snavely, N., Seitz, S., Szeliski, R.: Skeletal graphs for efficient structure from motion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Farenzena, M., Fusiello, A., Gherardi, R.: Structure-and-motion pipeline on a hierarchical cluster tree. In: IEEE International Workshop on 3-D Digital Imaging and Modeling, pp. 1489–1496 (2009)
Gherardi, R., Farenzena, M., Fusiello, A.: Improving the efficiency of hierarchical structure-and-motion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1594–1600 (June 2010)
Irschara, A., Zach, C., Frahm, J., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2599–2606 (2009)
Arth, C., Wagner, D., Klopschitz, M., Irschara, A., Schmalstieg, D.: Wide area localization on mobile phones. In: 8th IEEE International Symposium on Mixed and Augmented Reality, pp. 73–82 (2009)
Li, Y., Snavely, N., Huttenlocher, D.P.: Location recognition using prioritized feature matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010)
Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2d-to-3d matching. In: International Conference on Computer Vision, pp. 667–674 (2011)
Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012)
Sattler, T., Leibe, B., Kobbelt, L.: Improving image-based localization by active correspondence search. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 752–765. Springer, Heidelberg (2012)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision, pp. 1470–1477 (2003)
Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: International Workshop on Multimedia Information Retrieval, pp. 197–206 (2007)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Conference on Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)
Moreno-Noguer, F., Lepetit, V., Fua, P.: Accurate non-iterative o(n) solution to the pnp problem. In: Proceeding of the IEEE International Conference on Computer Vision (October 2007)
Fiore, P.D.: Efficient linear solution of exterior orientation. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 140–148 (2001)
Schweighofer, G., Pinz, A.: Globally optimal o(n) solution to the pnp problem for general camera models. In: British Machine Vision Conference, pp. 55.1–55.10 (2008)
Garro, V., Crosilla, F., Fusiello, A.: Solving the pnp problem with anisotropic orthogonal procrustes analysis. In: Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission (2012)
Bennani Dosse, M., Ten Berge, J.: Anisotropic orthogonal procrustes analysis. Journal of Classification 27(1), 111–128 (2010)
Torr, P.H.S., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. In: Computer Vision and Image Understanding, vol. 78 (2000)
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Garro, V., Galassi, M., Fusiello, A. (2013). Wide Area Camera Localization. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_33
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DOI: https://doi.org/10.1007/978-3-642-41181-6_33
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