A Revisit of Methods for Determining the Fundamental Matrix with Planes

Determining the fundamental matrix from a collection of inter-frame homographies (more than two) is a classical problem. The compatibility relationship between the fundamental matrix and any of the ideally consistent homographies can be used to compute the fundamental matrix. Using the direct linear transformation (DLT), the compatibility equation can be translated into a least squares problem and can be easily solved via SVD decomposition. However, this solution is extremely susceptible to noise and motion inconsistencies, hence rarely used. Inspired by the normalized eight-point algorithm, we show that a relatively simple but non-trivial two-step normalization of the input homographies achieves the desired effect, and the results are at last comparable to the less attractive hallucinated points method. The algorithm is theoretically justified and verified by experiments on both synthetic and real data.

[1]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  Andrew Zisserman,et al.  Matching and Reconstruction from Widely Separated Views , 1998, SMILE.

[5]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[6]  Internal Report : 2001-V 04 From Lines to Homographies between Uncalibrated Images , 2005 .

[7]  Amnon Shashua,et al.  The Rank 4 Constraint in Multiple (>=3) View Geometry , 1996, ECCV.

[8]  David Suter,et al.  Rank Constraints for Homographies over Two Views: Revisiting the Rank Four Constraint , 2009, International Journal of Computer Vision.

[9]  Henrik I. Christensen,et al.  Using the Relation Between a Plane Projectivity and the Fundamental Matrix , 1996 .

[10]  Lihi Zelnik-Manor,et al.  Multiview Constraints on Homographies , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Hui Zeng,et al.  A new normalized method on line-based homography estimation , 2008, Pattern Recognit. Lett..

[13]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[14]  I HartleyRichard In Defense of the Eight-Point Algorithm , 1997 .

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[17]  Josechu J. Guerrero,et al.  Robust Line Matching and Estimate of Homographies Simultaneously , 2003, IbPRIA.

[18]  Richard Szeliski,et al.  Geometrically Constrained Structure from Motion: Points on Planes , 1998, SMILE.

[19]  Olivier D. Faugeras,et al.  Determining the fundamental matrix with planes: instability and new algorithms , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Anders P. Eriksson,et al.  Sampson distance based joint estimation of multiple homographies with uncalibrated cameras , 2014, Comput. Vis. Image Underst..

[21]  Zygmunt L. Szpak,et al.  Enforcing consistency constraints in uncalibrated multiple homography estimation using latent variables , 2015, Machine Vision and Applications.

[22]  É. Vincent,et al.  Detecting planar homographies in an image pair , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[23]  Olivier D. Faugeras,et al.  The fundamental matrix: Theory, algorithms, and stability analysis , 2004, International Journal of Computer Vision.