The Community for Technology Leaders
RSS Icon
Issue No.01 - January (2011 vol.33)
pp: 186-193
Mahesh Ramachandran , University of Maryland, College Park
Ashok Veeraraghavan , Mitsubishi Electric Research Labs, Cambridge
Rama Chellappa , University of Maryland, College Park
In this paper, we study the benefits of the availability of a specific form of additional information—the vertical direction (gravity) and the height of the camera, both of which can be conveniently measured using inertial sensors and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The SfM algorithm developed in this paper is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research data set.
Structure from motion, multiple view geometry, computer vision.
Mahesh Ramachandran, Ashok Veeraraghavan, Rama Chellappa, "A Fast Bilinear Structure from Motion Algorithm Using a Video Sequence and Inertial Sensors", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 1, pp. 186-193, January 2011, doi:10.1109/TPAMI.2010.163
[1] H.C. Longuet-Higgins, "A Computer Algorithm for Reconstructing a Scene from Two Projections," Nature, vol. 293, no. 1, pp. 133-135, 1981.
[2] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed. Cambridge Univ. Press, 2004.
[3] K. Ni, D. Steedly, and F. Dellaert, "Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction," Proc. IEEE Int'l Conf. Computer Vision, pp. 1-8, Oct. 2007.
[4] M. Euston, P. Coote, R. Mahony, J. Kim, and T. Hamel, "A Complementary Filter for Attitude Estimation of a Fixed-Wing UAV," Proc. IEEE Int'l Conf. Robots and Systems, pp. 340-345, Sept. 2008.
[5] E. Malis and R. Cipolla, "Camera Calibration from Unknown Planar Structures Enforcing the Multi View Constraints between Collineations," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1268-1272, Sept. 2002.
[6] B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon, "Bundle Adjustment—A Modern Synthesis," Vision Algorithms: Theory and Practice, pp. 298-372, Springer-Verlag, 2000.
[7] K. Madsen, H. Nielsen, and O. Tingleff, Methods for Non-Linear Least Squares Problems, second ed. Technical Univ. of Denmark, 2004.
[8] M.I.A. Lourakis and A.A. Argyros, "SBA: A Software Package for Generic Sparse Bundle Adjustment," ACM Trans. Math. Software, vol. 36, pp. 1-30, Jan. 2009.
[9] M. Byrod and K. Astrom, "Bundle Adjustment Using Conjugate Gradients with Multiscale Preconditioning," Proc. British Machine Vision Conf., Sept. 2009.
[10] R. Carceroni, A. Kumar, and K. Daniilidis, "Structure from Motion with Known Camera Positions," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 477-484, June 2006.
[11] G. Qian, Q. Zheng, and R. Chellappa, "Reduction of Inherent Ambiguities in Structure from Motion Problem Using Inertial Data," Proc. Int'l Conf. Image Processing, vol. 1, pp. 204-207, 2000.
[12] P. Sturm and B. Triggs, "A Factorization-Based Algorithm for Multi-Image Projective Structure and Motion," Proc. European Conf. Computer Vision, vol. 1, pp. 709-720, Apr. 1996.
[13] S. Mahamud, M. Hebert, Y. Omori, and J. Ponce, "Provably-Convergent Iterative Methods for Projective Structure from Motion," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1018-1025, Dec. 2001.
[14] J. Oliensis and R. Hartley, "Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2217-2233, Dec. 2007.
[15] A. Buchanan and A. Fitzgibbon, "Damped Newton Algorithms for Matrix Factorization with Missing Data," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 316-322, June 2005.
[16] R. Kaucic, R. Hartley, and N. Dano, "Plane-Based Projective Reconstruction," Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 420-427, July 2001.
[17] C. Rother and S. Carlsson, "Linear Multi View Reconstruction and Camera Recovery Using a Reference Plane," Int'l J. Computer Vision, vol. 49, no. 3, pp. 117-141, Sept. 2002.
[18] M. Lourakis, "Levmar: Levenberg-Marquardt Nonlinear Least Squares Algorithms in C/C++,", July 2004.
[19] W.W. Hager and H. Zhang, "CG DESCENT: A Conjugate Gradient Method with Guaranteed Descent," ACM Trans. Math. Software, vol. 32, pp. 113-137, Mar. 2006.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool