Efficient Intersection of Three Quadrics and Applications in Computer Vision

In this paper, we present a new algorithm for finding all intersections of three quadrics. The proposed method is algebraic in nature and it is considerably more efficient than the Gröbner basis and resultant-based solutions previously used in computer vision applications. We identify several computer vision problems that are formulated and solved as systems of three quadratic equations and for which our algorithm readily delivers considerably faster results. Also, we propose new formulations of three important vision problems: absolute camera pose with unknown focal length, generalized pose-and-scale, and hand-eye calibration with known translation. These new formulations allow our algorithm to significantly outperform the state-of-the-art in speed.

[1]  Martin Byröd,et al.  A Column-Pivoting Based Strategy for Monomial Ordering in Numerical Gröbner Basis Calculations , 2008, ECCV.

[2]  Hongdong Li,et al.  An Efficient Hidden Variable Approach to Minimal-Case Camera Motion Estimation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zuzana Kukelova,et al.  Making minimal solvers fast , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Michael Kettner,et al.  Algorithmic and topological aspects of semi-algebraic sets defined by quadratic polynomial , 2007, ArXiv.

[6]  Yiu Cheung Shiu,et al.  Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX=XB , 1989, IEEE Trans. Robotics Autom..

[7]  Helder Araújo,et al.  Direct Solution to the Minimal Generalized Pose , 2015, IEEE Transactions on Cybernetics.

[8]  Ron Goldman,et al.  Using multivariate resultants to find the intersection of three quadric surfaces , 1991, TOGS.

[9]  Peter F. Sturm,et al.  Pose estimation using both points and lines for geo-localization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Yubin Kuang,et al.  Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Zuzana Kukelova,et al.  Fast and Stable Algebraic Solution to L2 Three-View Triangulation , 2013, 2013 International Conference on 3D Vision.

[12]  David Nister,et al.  Recent developments on direct relative orientation , 2006 .

[13]  M. Hazewinkel Encyclopaedia of mathematics , 1987 .

[14]  David Nistér,et al.  A Minimal Solution to the Generalised 3-Point Pose Problem , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[16]  Yang Guo A Novel Solution to the P4P Problem for an Uncalibrated Camera , 2012, Journal of Mathematical Imaging and Vision.

[17]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[18]  M. Reid,et al.  The complete intersection of two of more quadrics , 1972 .

[19]  B. Roth Computations in Kinematics , 1993 .

[20]  Zuzana Kukelova,et al.  A general solution to the P4P problem for camera with unknown focal length , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Jia-Guang Sun,et al.  A robust algorithm for finding the real intersections of three quadric surfaces , 2005, Comput. Aided Geom. Des..

[22]  Marina Weber,et al.  Using Algebraic Geometry , 2016 .

[23]  Chee-Keng Yap,et al.  Fundamental problems of algorithmic algebra , 1999 .

[24]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Zuzana Kukelova,et al.  Automatic Generator of Minimal Problem Solvers , 2008, ECCV.

[27]  Yuichi Taguchi,et al.  P2Pi: A Minimal Solution for Registration of 3D Points to 3D Planes , 2010, ECCV.

[28]  Peter F. Sturm,et al.  A generic structure-from-motion framework , 2006, Comput. Vis. Image Underst..

[29]  Zuzana Kukelova,et al.  Hand-Eye Calibration without Hand Orientation Measurement Using Minimal Solution , 2012, ACCV.

[30]  Sylvain Lazard,et al.  Near-Optimal Parameterization of the Intersection of Quadrics : III . Parameterizing Singular Intersections , 2005 .

[31]  Marc Pollefeys,et al.  Minimal Solutions for Pose Estimation of a Multi-Camera System , 2013, ISRR.

[32]  Dieter Schmalstieg,et al.  A Minimal Solution to the Generalized Pose-and-Scale Problem , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Joshua Z. Levin Mathematical models for determining the intersections of quadric surfaces , 1979 .

[34]  Zuzana Kukelova,et al.  Fast and robust numerical solutions to minimal problems for cameras with radial distortion , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Zuzana Kukelova,et al.  New Efficient Solution to the Absolute Pose Problem for Camera with Unknown Focal Length and Radial Distortion , 2010, ACCV.

[36]  Roger Y. Tsai,et al.  A new technique for fully autonomous and efficient 3D robotics hand/eye calibration , 1988, IEEE Trans. Robotics Autom..