R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

J. Morel and G. Yu, “Is SIFT scale invariant?” Inverse Problems Imaging 5, 115–136 (2011).

[CrossRef]

E. Rosten and R. Loveland, “Camera distortion self-calibration using the plumb-line constraint and minimal hough entropy,” Mach. Vision Appl. 22, 77–85 (2011).

L. G. Luis Alvarez and J. R. Sendra, “Algebraic lens distortion model estimation,” IPOP (2010), http://dx.doi.org/10.5201/ipol.2010.ags-alde .

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009).

[CrossRef]

F. Devernay and O. Faugeras, “Straight lines have to be straight,” Mach. Vision Appl. 13, 14–24 (2001).

[CrossRef]

Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).

[CrossRef]

B. Prescott and G. McLean, “Line-based correction of radial lens distortion,” Graph. Mod. Image Process. 59, 39–47(1997).

[CrossRef]

M. H. J. Weng and P. Cohen, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).

[CrossRef]

R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE J. Robot. Autom. 3, 323–344 (1987).

[CrossRef]

R. Deriche, “Using Canny’s criteria to derive a recursively implemented optimal edge detector,” Int. J. Comput. Vis. 1, 167–187 (1987).

[CrossRef]

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698(1986).

[CrossRef]

D. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009).

[CrossRef]

L. G. Luis Alvarez and J. R. Sendra, “Algebraic lens distortion model estimation,” IPOP (2010), http://dx.doi.org/10.5201/ipol.2010.ags-alde .

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

J. Barreto and K. Daniilidis, “Fundamental matrix for cameras with radial distortion,” in Proceedings of Tenth IEEE International Conference on Computer Vision (IEEE, 2005), pp. 625–632.

D. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

Z. Kukelova, M. Bujnak, and T. Pajdla, “Automatic generator of minimal problem solvers,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 302–315.

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

K. Josephson and M. Byrod, “Pose estimation with radial distortion and unknown focal length,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 2419–2426.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698(1986).

[CrossRef]

D. Claus and A. Fitzgibbon, “A plumbline constraint for the rational function lens distortion model,” in Proceedings of British Machine Vision Conference (2005), pp. 99–108.

D. Claus and A. Fitzgibbon, “A rational function lens distortion model for general cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 213–219.

M. H. J. Weng and P. Cohen, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).

[CrossRef]

J. Barreto and K. Daniilidis, “Fundamental matrix for cameras with radial distortion,” in Proceedings of Tenth IEEE International Conference on Computer Vision (IEEE, 2005), pp. 625–632.

R. Deriche, “Using Canny’s criteria to derive a recursively implemented optimal edge detector,” Int. J. Comput. Vis. 1, 167–187 (1987).

[CrossRef]

F. Devernay and O. Faugeras, “Straight lines have to be straight,” Mach. Vision Appl. 13, 14–24 (2001).

[CrossRef]

F. Devernay, “A non-maxima suppression method for edge detection with sub-pixel accuracy,” Tech. Rep. 2724, (INRIA rapport de recherche, 1995).

J. Lavest, M. Viala, and M. Dhome, “Do we really need accurate calibration pattern to achieve a reliable camera calibration?” in Computer Vision—ECCV’98, Vol. 1408 of Lecture Notes in Computer Science (Springer, 1998), pp. 158–174.

F. Devernay and O. Faugeras, “Straight lines have to be straight,” Mach. Vision Appl. 13, 14–24 (2001).

[CrossRef]

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.

D. Claus and A. Fitzgibbon, “A plumbline constraint for the rational function lens distortion model,” in Proceedings of British Machine Vision Conference (2005), pp. 99–108.

A. Fitzgibbon, “Simultaneous linear estimation of multiple view geometry and lens distortion,” in Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 125–132.

D. Claus and A. Fitzgibbon, “A rational function lens distortion model for general cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 213–219.

L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009).

[CrossRef]

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2004).

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.

H. Li and R. Hartley, “A non-iterative method for correcting lens distortion from nine-point correspondences,” in Proceedings OmniVision ’05, ICCV Workshop (2005).

T. Pajdla, T. Werner, and V. Hlavac, “Correcting radial lens distortion without knowledge of 3-D structure,” Research Report (Czech Technical University, 1997).

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

K. Josephson and M. Byrod, “Pose estimation with radial distortion and unknown focal length,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 2419–2426.

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

Z. Kukelova and T. Pajdla, “A minimal solution to the autocalibration of radial distortion,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–7.

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

Z. Kukelova and T. Pajdla, “Two minimal problems for cameras with radial distortion,” in Proceedings of 11th IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Z. Kukelova, M. Bujnak, and T. Pajdla, “Automatic generator of minimal problem solvers,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 302–315.

J. Lavest, M. Viala, and M. Dhome, “Do we really need accurate calibration pattern to achieve a reliable camera calibration?” in Computer Vision—ECCV’98, Vol. 1408 of Lecture Notes in Computer Science (Springer, 1998), pp. 158–174.

H. Li and R. Hartley, “A non-iterative method for correcting lens distortion from nine-point correspondences,” in Proceedings OmniVision ’05, ICCV Workshop (2005).

E. Rosten and R. Loveland, “Camera distortion self-calibration using the plumb-line constraint and minimal hough entropy,” Mach. Vision Appl. 22, 77–85 (2011).

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.

B. Prescott and G. McLean, “Line-based correction of radial lens distortion,” Graph. Mod. Image Process. 59, 39–47(1997).

[CrossRef]

B. Micusik and T. Pajdla, “Estimation of omnidirectional camera model from epipolar geometry,” in Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 485–490.

R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

J. Morel and G. Yu, “Is SIFT scale invariant?” Inverse Problems Imaging 5, 115–136 (2011).

[CrossRef]

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.

B. Micusik and T. Pajdla, “Estimation of omnidirectional camera model from epipolar geometry,” in Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 485–490.

Z. Kukelova and T. Pajdla, “Two minimal problems for cameras with radial distortion,” in Proceedings of 11th IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Z. Kukelova, M. Bujnak, and T. Pajdla, “Automatic generator of minimal problem solvers,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 302–315.

Z. Kukelova and T. Pajdla, “A minimal solution to the autocalibration of radial distortion,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–7.

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

T. Pajdla, T. Werner, and V. Hlavac, “Correcting radial lens distortion without knowledge of 3-D structure,” Research Report (Czech Technical University, 1997).

S. Thirthala and M. Pollefeys, “The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 321–328.

B. Prescott and G. McLean, “Line-based correction of radial lens distortion,” Graph. Mod. Image Process. 59, 39–47(1997).

[CrossRef]

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

E. Rosten and R. Loveland, “Camera distortion self-calibration using the plumb-line constraint and minimal hough entropy,” Mach. Vision Appl. 22, 77–85 (2011).

L. G. Luis Alvarez and J. R. Sendra, “Algebraic lens distortion model estimation,” IPOP (2010), http://dx.doi.org/10.5201/ipol.2010.ags-alde .

L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009).

[CrossRef]

C. Slama, Manual of Photogrammetry, 4th ed. (American Society of Photogrammetry, 1980).

G. P. Stein, “Lens distortion calibration using point correspondences,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 602–608.

R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.

Z. Tang, “Calibration de caméra à haute précision,” Ph.D. dissertation (Ecole Normale Supérieure de Cachan, 2011).

S. Thirthala and M. Pollefeys, “The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 321–328.

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.

R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE J. Robot. Autom. 3, 323–344 (1987).

[CrossRef]

J. Lavest, M. Viala, and M. Dhome, “Do we really need accurate calibration pattern to achieve a reliable camera calibration?” in Computer Vision—ECCV’98, Vol. 1408 of Lecture Notes in Computer Science (Springer, 1998), pp. 158–174.

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.

M. H. J. Weng and P. Cohen, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).

[CrossRef]

T. Pajdla, T. Werner, and V. Hlavac, “Correcting radial lens distortion without knowledge of 3-D structure,” Research Report (Czech Technical University, 1997).

J. Morel and G. Yu, “Is SIFT scale invariant?” Inverse Problems Imaging 5, 115–136 (2011).

[CrossRef]

Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).

[CrossRef]

Z. Zhang, “On the epipolar geometry between two images with lens distortion,” in Proceedings of 13th International Conference on Pattern Recognition (IEEE, 1996), pp. 407–411.

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2004).

B. Prescott and G. McLean, “Line-based correction of radial lens distortion,” Graph. Mod. Image Process. 59, 39–47(1997).

[CrossRef]

R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE J. Robot. Autom. 3, 323–344 (1987).

[CrossRef]

Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).

[CrossRef]

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698(1986).

[CrossRef]

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010).

[CrossRef]

M. H. J. Weng and P. Cohen, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).

[CrossRef]

R. Deriche, “Using Canny’s criteria to derive a recursively implemented optimal edge detector,” Int. J. Comput. Vis. 1, 167–187 (1987).

[CrossRef]

J. Morel and G. Yu, “Is SIFT scale invariant?” Inverse Problems Imaging 5, 115–136 (2011).

[CrossRef]

L. G. Luis Alvarez and J. R. Sendra, “Algebraic lens distortion model estimation,” IPOP (2010), http://dx.doi.org/10.5201/ipol.2010.ags-alde .

R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .

L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009).

[CrossRef]

E. Rosten and R. Loveland, “Camera distortion self-calibration using the plumb-line constraint and minimal hough entropy,” Mach. Vision Appl. 22, 77–85 (2011).

F. Devernay and O. Faugeras, “Straight lines have to be straight,” Mach. Vision Appl. 13, 14–24 (2001).

[CrossRef]

D. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

C. Slama, Manual of Photogrammetry, 4th ed. (American Society of Photogrammetry, 1980).

J. Lavest, M. Viala, and M. Dhome, “Do we really need accurate calibration pattern to achieve a reliable camera calibration?” in Computer Vision—ECCV’98, Vol. 1408 of Lecture Notes in Computer Science (Springer, 1998), pp. 158–174.

D. Claus and A. Fitzgibbon, “A plumbline constraint for the rational function lens distortion model,” in Proceedings of British Machine Vision Conference (2005), pp. 99–108.

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2004).

T. Pajdla, T. Werner, and V. Hlavac, “Correcting radial lens distortion without knowledge of 3-D structure,” Research Report (Czech Technical University, 1997).

F. Devernay, “A non-maxima suppression method for edge detection with sub-pixel accuracy,” Tech. Rep. 2724, (INRIA rapport de recherche, 1995).

Z. Tang, “Calibration de caméra à haute précision,” Ph.D. dissertation (Ecole Normale Supérieure de Cachan, 2011).

R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.

G. P. Stein, “Lens distortion calibration using point correspondences,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 602–608.

Z. Zhang, “On the epipolar geometry between two images with lens distortion,” in Proceedings of 13th International Conference on Pattern Recognition (IEEE, 1996), pp. 407–411.

A. Fitzgibbon, “Simultaneous linear estimation of multiple view geometry and lens distortion,” in Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 125–132.

B. Micusik and T. Pajdla, “Estimation of omnidirectional camera model from epipolar geometry,” in Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 485–490.

H. Li and R. Hartley, “A non-iterative method for correcting lens distortion from nine-point correspondences,” in Proceedings OmniVision ’05, ICCV Workshop (2005).

S. Thirthala and M. Pollefeys, “The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 321–328.

D. Claus and A. Fitzgibbon, “A rational function lens distortion model for general cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 213–219.

J. Barreto and K. Daniilidis, “Fundamental matrix for cameras with radial distortion,” in Proceedings of Tenth IEEE International Conference on Computer Vision (IEEE, 2005), pp. 625–632.

Z. Kukelova and T. Pajdla, “Two minimal problems for cameras with radial distortion,” in Proceedings of 11th IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Z. Kukelova, M. Bujnak, and T. Pajdla, “Automatic generator of minimal problem solvers,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 302–315.

M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.

Z. Kukelova and T. Pajdla, “A minimal solution to the autocalibration of radial distortion,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–7.

K. Josephson and M. Byrod, “Pose estimation with radial distortion and unknown focal length,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 2419–2426.

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.