Pose Estimation for General Cameras Using Lines

In this paper, we address the problem of pose estimation under the framework of generalized camera models. We propose a solution based on the knowledge of the coordinates of 3-D straight lines (expressed in the world coordinate frame) and their corresponding image pixels. Previous approaches used the knowledge of the coordinates of 3-D points (zero dimensional elements) and their corresponding images (zero dimensional elements). In this paper, pixels belonging to the image of 3-D lines are used. There is no need to establish correspondences between pixels and 3-D points. Correspondences are established between 3-D lines and their images. There is no need to identify individual pixels. The use of correspondences between pixels (that belong to the images of the 3-D lines) and 3-D lines facilitates the correspondence problem when compared to the use of world and image points. This is one of the contributions of this paper. The approach is both evaluated and validated using synthetic data and also real images.

[1]  Shree K. Nayar,et al.  The Raxel Imaging Model and Ray-Based Calibration , 2005, International Journal of Computer Vision.

[2]  Kostas Daniilidis,et al.  Linear Pose Estimation from Points or Lines , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Helder Araújo,et al.  Calibration of Smooth Camera Models , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  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..

[5]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Douglas Lanman,et al.  Reconstructing a 3D Line from a Single Catadioptric Image , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[7]  Axel Pinz,et al.  Globally Optimal O(n) Solution to the PnP Problem for General Camera Models , 2008, BMVC.

[8]  Seth J. Teller,et al.  Determining the Lines Through Four Lines , 1999, J. Graphics, GPU, & Game Tools.

[9]  Robert Pless,et al.  Using many cameras as one , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Helder Araújo,et al.  Efficient Iterative Pose Estimation Using an Invariant to Rotations , 2014, IEEE Transactions on Cybernetics.

[11]  Y.Y. Schechner,et al.  Flat refractive geometry , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Shree K. Nayar,et al.  Planar catadioptric stereo: geometry and calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[13]  Peter F. Sturm,et al.  A Generic Concept for Camera Calibration , 2004, ECCV.

[14]  Nuno Gonçalves On the reflection point where light reflects to a known destination on quadratic surfaces. , 2010, Optics letters.

[15]  Wen-Yan Chang,et al.  Pose estimation for generalized imaging device via solving non-perspective N point problem , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[16]  Helder Araújo,et al.  A Fully Projective Formulation to Improve the Accuracy of Lowe's Pose-Estimation Algorithm , 1998, Comput. Vis. Image Underst..

[17]  Helder Araújo,et al.  Reconstruction of 3D lines from a single axial catadioptric image using cross-ratio , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[18]  Peter F. Sturm,et al.  Multi-view geometry for general camera models , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  H. Pottmann,et al.  Computational Line Geometry , 2001 .

[20]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Vincenzo Caglioti,et al.  Line Localization from Single Catadioptric Images , 2011, International Journal of Computer Vision.

[22]  Vincent Lepetit,et al.  Accurate Non-Iterative O(n) Solution to the PnP Problem , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[23]  P. Schönemann,et al.  A generalized solution of the orthogonal procrustes problem , 1966 .

[24]  Joel A. Hesch,et al.  A Direct Least-Squares (DLS) method for PnP , 2011, 2011 International Conference on Computer Vision.

[25]  Yuichi Taguchi,et al.  Beyond Alhazen's problem: Analytical projection model for non-central catadioptric cameras with quadric mirrors , 2011, CVPR 2011.

[26]  Vincenzo Caglioti,et al.  On the localization of straight lines in 3D space from single 2D images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Shree K. Nayar,et al.  Non-Single Viewpoint Catadioptric Cameras: Geometry and Analysis , 2006, International Journal of Computer Vision.

[28]  Rahul Swaminathan,et al.  Depth From Distortions , 2008 .

[29]  Andrew Zisserman,et al.  Multiple View Geometry , 1999 .

[30]  Xinhua Zhuang,et al.  Pose estimation from corresponding point data , 1989, IEEE Trans. Syst. Man Cybern..

[31]  Wen-Yan Chang,et al.  On pose recovery for generalized visual sensors , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Adrien Bartoli,et al.  Structure-from-motion using lines: Representation, triangulation, and bundle adjustment , 2005, Comput. Vis. Image Underst..