Abstract

Acquiring a high-accuracy three-dimensional (3D) shape of a large-scale object from multiple uncalibrated camera views remains a big challenge, since a considerable number of images is required to cover the entire surface; the use of multiple images could, however, result in accumulative errors from each processed image. Here error propagation rules in the 3D reconstruction process have been deduced on the basis of the traditional dual-view reconstruction method. We propose a method that can control the accumulative errors by reducing the times of coordinate transformation with common-view-based dual-view reconstruction. This method involves constructing an image network composed of many image groups, each of which contains a common view. A baseline threshold method is introduced to construct a high-quality image network, and the sums or reprojection residual of all the common points is proposed to assess the validity of the solutions of the orientation. Experiments carried out with both synthetic and real images demonstrate that the proposed method can handle the accumulative error problem with robust and highly accurate results.

© 2011 Optical Society of America

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  1. B. K. P. Horn, “Relative orientation,” Int. J. Comput. Vis. 4, 59–78 (1990).
    [CrossRef]
  2. R. I. Hartley, “Estimation of relative camera positions for uncalibrated cameras,” in Proceedings of European Conference on Computer Vision (Springer, 1992), pp. 579–587.
  3. S. Prakoonwit and R. Benjamin, “3D surface point and wire frame reconstruction from multiview photo graphic images,” Image Vis. Comput. 25, 1509–1518 (2007).
    [CrossRef]
  4. P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
    [CrossRef]
  5. K. Y. K. Wong and R. Cipolla, “Reconstruction of sculpture from its proles with unknown camera positions,” IEEE Trans. Image Process. 13, 381–389 (2004).
    [CrossRef] [PubMed]
  6. P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.
  7. R. Koch, M. Pollefeys, and L. Van Gool, “Multiview point stereo from uncalibrated sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1998), pp. 55–71.
  8. M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.
  9. F. C. M. Martins and J. M. F. Moura, “Video representation with 3D entities,” IEEE J. Sel. Areas Commun. 16, 71–85(1998).
    [CrossRef]
  10. P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.
  11. S. Cronk, C. S. Fraser, and H. Hanley, “Automatic calibration of color digital cameras,” in The Photogrammetric Record (2006), pp. 355–372.
    [CrossRef]
  12. C. S. Fraser, “Automated processes in digital photogrammetric calibration, orientation, and triangulation,” Digit. Signal Process. 8, 277–283 (1998).
    [CrossRef]
  13. C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: a factorization method,” lnt. J. Comput. Vis. 9, 137–154 (1992).
    [CrossRef]
  14. C. J. Poelman and T. Kanade, “A parapersective factorization method for shape and motion recovery,” in Proceedings of European Conference on Computer Vision (Springer, 1994), pp. 97–108.
  15. R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2004).
    [CrossRef]
  16. D. Jacobs, “Linear fitting with missing data: applications to structure from motion and to characterizing intensity images,” in Proceedings of IEEE Conference on CVPR (IEEE, 1997), pp. 206–212.
  17. D. Martinec and T. Pajdla, “Structure from many perspective images with occlusions,” in Proceedings of the European Conference on Computer Vision (Springer, 2002), pp. 355–369.
  18. K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
    [CrossRef]
  19. B. K. P. Horn, “Closed-form solution of absolute orientation using unit quaternions,” J. Opt. Soc. Am. A 4, 629–642 (1987).
    [CrossRef]
  20. B. K. P. Horn, H. M. Hilden, and S. Negahdaripour, “Closed-form solution of absolute orientation using orthonormal matrices,” J. Opt. Soc. Am. A 5, 1127–1135 (1988).
    [CrossRef]
  21. M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
    [CrossRef]
  22. D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Machine Vis. Apps. 9, 272–290 (1997).
    [CrossRef]
  23. M. Sun, R. X. He, and D. J. Wang, “Precision analysis to 3D reconstruction from image sequences,” in Proceedings of ISPRS Workshop on Updating Geo-spatial Databases with Imagery & DMGIS (2007), pp. 141–146.
  24. D. Batra, B. Nabbe, and M. Hebert, “An alternative formulation for five point relative pose problem,” in Proceedings of the IEEE Workshop on Motion and Video Computing (IEEE, 2007), pp. 21–26.
    [CrossRef]
  25. H. D. Li and R. I. Hartley, “Five-point motion estimation made easy,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2006), pp. 630–633.
  26. S. Segvic, G. Schweighofer, and A. Pinz, “Influence of numerical conditioning on the accuracy of relative orientation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
  27. G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.
  28. J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
    [CrossRef]
  29. V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in Proceedings of International Society for Photogrammetry and Remote Sensing (2008), pp. 135–140.

2009 (1)

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

2008 (1)

V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in Proceedings of International Society for Photogrammetry and Remote Sensing (2008), pp. 135–140.

2007 (4)

S. Segvic, G. Schweighofer, and A. Pinz, “Influence of numerical conditioning on the accuracy of relative orientation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

M. Sun, R. X. He, and D. J. Wang, “Precision analysis to 3D reconstruction from image sequences,” in Proceedings of ISPRS Workshop on Updating Geo-spatial Databases with Imagery & DMGIS (2007), pp. 141–146.

D. Batra, B. Nabbe, and M. Hebert, “An alternative formulation for five point relative pose problem,” in Proceedings of the IEEE Workshop on Motion and Video Computing (IEEE, 2007), pp. 21–26.
[CrossRef]

S. Prakoonwit and R. Benjamin, “3D surface point and wire frame reconstruction from multiview photo graphic images,” Image Vis. Comput. 25, 1509–1518 (2007).
[CrossRef]

2006 (2)

S. Cronk, C. S. Fraser, and H. Hanley, “Automatic calibration of color digital cameras,” in The Photogrammetric Record (2006), pp. 355–372.
[CrossRef]

H. D. Li and R. I. Hartley, “Five-point motion estimation made easy,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2006), pp. 630–633.

2004 (2)

K. Y. K. Wong and R. Cipolla, “Reconstruction of sculpture from its proles with unknown camera positions,” IEEE Trans. Image Process. 13, 381–389 (2004).
[CrossRef] [PubMed]

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

2002 (1)

D. Martinec and T. Pajdla, “Structure from many perspective images with occlusions,” in Proceedings of the European Conference on Computer Vision (Springer, 2002), pp. 355–369.

2000 (1)

P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
[CrossRef]

1998 (4)

R. Koch, M. Pollefeys, and L. Van Gool, “Multiview point stereo from uncalibrated sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1998), pp. 55–71.

M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.

F. C. M. Martins and J. M. F. Moura, “Video representation with 3D entities,” IEEE J. Sel. Areas Commun. 16, 71–85(1998).
[CrossRef]

C. S. Fraser, “Automated processes in digital photogrammetric calibration, orientation, and triangulation,” Digit. Signal Process. 8, 277–283 (1998).
[CrossRef]

1997 (2)

D. Jacobs, “Linear fitting with missing data: applications to structure from motion and to characterizing intensity images,” in Proceedings of IEEE Conference on CVPR (IEEE, 1997), pp. 206–212.

D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Machine Vis. Apps. 9, 272–290 (1997).
[CrossRef]

1996 (2)

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

1994 (1)

C. J. Poelman and T. Kanade, “A parapersective factorization method for shape and motion recovery,” in Proceedings of European Conference on Computer Vision (Springer, 1994), pp. 97–108.

1992 (2)

R. I. Hartley, “Estimation of relative camera positions for uncalibrated cameras,” in Proceedings of European Conference on Computer Vision (Springer, 1992), pp. 579–587.

C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: a factorization method,” lnt. J. Comput. Vis. 9, 137–154 (1992).
[CrossRef]

1991 (1)

M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
[CrossRef]

1990 (1)

B. K. P. Horn, “Relative orientation,” Int. J. Comput. Vis. 4, 59–78 (1990).
[CrossRef]

1989 (1)

J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
[CrossRef]

1988 (1)

1987 (2)

K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
[CrossRef]

B. K. P. Horn, “Closed-form solution of absolute orientation using unit quaternions,” J. Opt. Soc. Am. A 4, 629–642 (1987).
[CrossRef]

Ahuja, N.

J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
[CrossRef]

Arun, K. S.

K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
[CrossRef]

Batra, D.

D. Batra, B. Nabbe, and M. Hebert, “An alternative formulation for five point relative pose problem,” in Proceedings of the IEEE Workshop on Motion and Video Computing (IEEE, 2007), pp. 21–26.
[CrossRef]

Beardsley, P.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

Benjamin, R.

S. Prakoonwit and R. Benjamin, “3D surface point and wire frame reconstruction from multiview photo graphic images,” Image Vis. Comput. 25, 1509–1518 (2007).
[CrossRef]

Blostein, S. D.

K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
[CrossRef]

Cipolla, R.

K. Y. K. Wong and R. Cipolla, “Reconstruction of sculpture from its proles with unknown camera positions,” IEEE Trans. Image Process. 13, 381–389 (2004).
[CrossRef] [PubMed]

Cronk, S.

S. Cronk, C. S. Fraser, and H. Hanley, “Automatic calibration of color digital cameras,” in The Photogrammetric Record (2006), pp. 355–372.
[CrossRef]

Eggert, D. W.

D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Machine Vis. Apps. 9, 272–290 (1997).
[CrossRef]

Eisert, P.

P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
[CrossRef]

Feng, Q. Y.

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

Fisher, R. B.

D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Machine Vis. Apps. 9, 272–290 (1997).
[CrossRef]

Fraser, C. S.

S. Cronk, C. S. Fraser, and H. Hanley, “Automatic calibration of color digital cameras,” in The Photogrammetric Record (2006), pp. 355–372.
[CrossRef]

C. S. Fraser, “Automated processes in digital photogrammetric calibration, orientation, and triangulation,” Digit. Signal Process. 8, 277–283 (1998).
[CrossRef]

Girod, B.

P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
[CrossRef]

Hanley, H.

S. Cronk, C. S. Fraser, and H. Hanley, “Automatic calibration of color digital cameras,” in The Photogrammetric Record (2006), pp. 355–372.
[CrossRef]

Hartley, R. I.

H. D. Li and R. I. Hartley, “Five-point motion estimation made easy,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2006), pp. 630–633.

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

R. I. Hartley, “Estimation of relative camera positions for uncalibrated cameras,” in Proceedings of European Conference on Computer Vision (Springer, 1992), pp. 579–587.

He, R. X.

M. Sun, R. X. He, and D. J. Wang, “Precision analysis to 3D reconstruction from image sequences,” in Proceedings of ISPRS Workshop on Updating Geo-spatial Databases with Imagery & DMGIS (2007), pp. 141–146.

Hebert, M.

D. Batra, B. Nabbe, and M. Hebert, “An alternative formulation for five point relative pose problem,” in Proceedings of the IEEE Workshop on Motion and Video Computing (IEEE, 2007), pp. 21–26.
[CrossRef]

Heinrichs, M.

V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in Proceedings of International Society for Photogrammetry and Remote Sensing (2008), pp. 135–140.

Hellwich, O.

V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in Proceedings of International Society for Photogrammetry and Remote Sensing (2008), pp. 135–140.

Hilden, H. M.

Horn, B. K. P.

Huang, T. S.

J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
[CrossRef]

K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
[CrossRef]

Jacobs, D.

D. Jacobs, “Linear fitting with missing data: applications to structure from motion and to characterizing intensity images,” in Proceedings of IEEE Conference on CVPR (IEEE, 1997), pp. 206–212.

Kanade, T.

C. J. Poelman and T. Kanade, “A parapersective factorization method for shape and motion recovery,” in Proceedings of European Conference on Computer Vision (Springer, 1994), pp. 97–108.

C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: a factorization method,” lnt. J. Comput. Vis. 9, 137–154 (1992).
[CrossRef]

Koch, R.

R. Koch, M. Pollefeys, and L. Van Gool, “Multiview point stereo from uncalibrated sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1998), pp. 55–71.

M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.

Li, H. D.

H. D. Li and R. I. Hartley, “Five-point motion estimation made easy,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2006), pp. 630–633.

Liu, G. H.

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

Liu, X. Y.

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

Lorusso, A.

D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Machine Vis. Apps. 9, 272–290 (1997).
[CrossRef]

Martinec, D.

D. Martinec and T. Pajdla, “Structure from many perspective images with occlusions,” in Proceedings of the European Conference on Computer Vision (Springer, 2002), pp. 355–369.

Martins, F. C. M.

F. C. M. Martins and J. M. F. Moura, “Video representation with 3D entities,” IEEE J. Sel. Areas Commun. 16, 71–85(1998).
[CrossRef]

Moura, J. M. F.

F. C. M. Martins and J. M. F. Moura, “Video representation with 3D entities,” IEEE J. Sel. Areas Commun. 16, 71–85(1998).
[CrossRef]

Nabbe, B.

D. Batra, B. Nabbe, and M. Hebert, “An alternative formulation for five point relative pose problem,” in Proceedings of the IEEE Workshop on Motion and Video Computing (IEEE, 2007), pp. 21–26.
[CrossRef]

Negahdaripour, S.

Pajdla, T.

D. Martinec and T. Pajdla, “Structure from many perspective images with occlusions,” in Proceedings of the European Conference on Computer Vision (Springer, 2002), pp. 355–369.

Pinz, A.

S. Segvic, G. Schweighofer, and A. Pinz, “Influence of numerical conditioning on the accuracy of relative orientation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Poelman, C. J.

C. J. Poelman and T. Kanade, “A parapersective factorization method for shape and motion recovery,” in Proceedings of European Conference on Computer Vision (Springer, 1994), pp. 97–108.

Pollefeys, M.

M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.

R. Koch, M. Pollefeys, and L. Van Gool, “Multiview point stereo from uncalibrated sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1998), pp. 55–71.

Prakoonwit, S.

S. Prakoonwit and R. Benjamin, “3D surface point and wire frame reconstruction from multiview photo graphic images,” Image Vis. Comput. 25, 1509–1518 (2007).
[CrossRef]

Rodehorst, V.

V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in Proceedings of International Society for Photogrammetry and Remote Sensing (2008), pp. 135–140.

Schweighofer, G.

S. Segvic, G. Schweighofer, and A. Pinz, “Influence of numerical conditioning on the accuracy of relative orientation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Segvic, S.

S. Segvic, G. Schweighofer, and A. Pinz, “Influence of numerical conditioning on the accuracy of relative orientation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Shao, L.

M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
[CrossRef]

Steinbach, E.

P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
[CrossRef]

Sun, M.

M. Sun, R. X. He, and D. J. Wang, “Precision analysis to 3D reconstruction from image sequences,” in Proceedings of ISPRS Workshop on Updating Geo-spatial Databases with Imagery & DMGIS (2007), pp. 141–146.

Tomasi, C.

C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: a factorization method,” lnt. J. Comput. Vis. 9, 137–154 (1992).
[CrossRef]

Torr, P.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

Van Gool, L.

R. Koch, M. Pollefeys, and L. Van Gool, “Multiview point stereo from uncalibrated sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1998), pp. 55–71.

M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.

Vergauwen, M.

M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool, “Flexible acquisition of 3D structure from motion,” in Proceedings of 10th IMDSP Workshop (IEEE, 1998), pp. 195–198.

Volz, R. A.

M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
[CrossRef]

Walker, M. W.

M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
[CrossRef]

Wang, D. J.

M. Sun, R. X. He, and D. J. Wang, “Precision analysis to 3D reconstruction from image sequences,” in Proceedings of ISPRS Workshop on Updating Geo-spatial Databases with Imagery & DMGIS (2007), pp. 141–146.

Wang, W. B.

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

Weng, J. Y.

J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
[CrossRef]

Wong, K. Y. K.

K. Y. K. Wong and R. Cipolla, “Reconstruction of sculpture from its proles with unknown camera positions,” IEEE Trans. Image Process. 13, 381–389 (2004).
[CrossRef] [PubMed]

Yuan, J. Y.

G. H. Liu, W. B. Wang, J. Y. Yuan, X. Y. Liu, and Q. Y. Feng, “A novel camera calibration method of variable focal length based on single-view,” in Proceedings of IEEE Conference on Electronic Commerce and Security (IEEE, 2009), pp. 775–778.

Zisserman, A.

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

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

P. Beardsley, P. Torr, and A. Zisserman, “3D model acquisition from extended image sequences,” in Proceedings of European Conference on Computer Vision (Springer, 1996), pp. 683–695.

CVGIP, Image Underst. (1)

M. W. Walker, L. Shao, and R. A. Volz, “Estimating 3-D location parameters using dual number quaternions,” CVGIP, Image Underst. 54, 358–367 (1991).
[CrossRef]

Digit. Signal Process. (1)

C. S. Fraser, “Automated processes in digital photogrammetric calibration, orientation, and triangulation,” Digit. Signal Process. 8, 277–283 (1998).
[CrossRef]

IEEE J. Sel. Areas Commun. (1)

F. C. M. Martins and J. M. F. Moura, “Video representation with 3D entities,” IEEE J. Sel. Areas Commun. 16, 71–85(1998).
[CrossRef]

IEEE Trans. Circuits Syst. Video Technol. (1)

P. Eisert, E. Steinbach, and B. Girod, “Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views,” IEEE Trans. Circuits Syst. Video Technol. 10, 261–272 (2000).
[CrossRef]

IEEE Trans. Image Process. (1)

K. Y. K. Wong and R. Cipolla, “Reconstruction of sculpture from its proles with unknown camera positions,” IEEE Trans. Image Process. 13, 381–389 (2004).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Machine Intell. (2)

K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987).
[CrossRef]

J. Y. Weng, T. S. Huang, and N. Ahuja, “Motion and structure from two perspective views: algorithms, error analysis, and error estimation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 451–476 (1989).
[CrossRef]

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Figures (17)

Fig. 1
Fig. 1

Measurement diagram.

Fig. 2
Fig. 2

Coordinate system.

Fig. 3
Fig. 3

Traditional reconstruction strategy.

Fig. 4
Fig. 4

Setup for taking pictures based on common view.

Fig. 5
Fig. 5

Reconstruction method based on common view.

Fig. 6
Fig. 6

Relative orientation of unoriented cameras.

Fig. 7
Fig. 7

Filtering the image network based on baseline threshold.

Fig. 8
Fig. 8

Camera model of image vectors.

Fig. 9
Fig. 9

Comparison of 3D average measurement error of two strategies.

Fig. 10
Fig. 10

(a) Rotation angle error and (b) translation vector error of the camera of the two strategies versus different noise level.

Fig. 11
Fig. 11

Two images of the car captured by the camera.

Fig. 12
Fig. 12

Top view of 3D target points of the car recovered by the (a) proposed strategy and (b) traditional strategy .

Fig. 13
Fig. 13

Side view of 3D target points of the car recovered by the (a) proposed strategy and (b) traditional strategy.

Fig. 14
Fig. 14

(a) 3D skeleton assembly model of the proposed strategy and (b) registration effect of 3D surface patches according to the skeleton assembly modelof the proposed strategy.

Fig. 15
Fig. 15

Four images of the desk.

Fig. 16
Fig. 16

3D skeleton assembly models reconstructed by the (a) proposed strategy and (b) traditional strategy.

Fig. 17
Fig. 17

Registration effect of 3D surface patches of the desk according to the 3D skeleton assembly model of the proposed strategy.

Tables (2)

Tables Icon

Table 1 Comparison of the Three Types of Error of the Two Reconstruction Strategies (in mm)

Tables Icon

Table 2 Comparison of the Measurement of the Desk by the Two Methods

Equations (11)

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d i 1 = R d i + T + V i ,
2 = i = 1 N d i R ¯ d i 1 T ¯ 2 .
{ d ¯ 1 = 1 N i = 1 N ( d i + Δ d i ) d c i = d 1 i d ¯ 1 d ¯ 2 = 1 N i = 1 N ( d i 1 + Δ d i 1 ) d c ( i 1 ) = d i 1 d ¯ 2 ,
2 = i = 1 N d c i R ¯ d c ( i 1 ) 2 = i = 1 N ( d c i T d c i + d c ( i 1 ) T d c ( i 1 ) 2 d c i T R ¯ d c ( i 1 ) ) .
H + Δ H = i = 1 N d c ( i 1 ) d c i T ,
SVD ( H + Δ H ) = ( U + Δ U ) ( Λ + Δ Λ ) ( V + Δ V ) T .
{ R = ( V + Δ V ) ( U + Δ U ) T = V U + V Δ U T + Δ V ( U T + Δ U T ) Δ R = R ¯ + Δ R T = ( d 1 ¯ + Δ d 1 ¯ ) ( R ¯ + Δ R ) ( d 2 ¯ + Δ d 2 ¯ ) = d 1 ¯ R ¯ d 2 ¯ + Δ d 1 ¯ R ¯ Δ d 2 ¯ Δ R ( d 2 ¯ + Δ d 2 ¯ ) Δ T = T ¯ + Δ T .
New _ D i = ( R + Δ R ) ( d i + Δ d i ) + ( T + Δ T ) = R d i + T + R Δ d i + Δ R ( d i + Δ d i ) + Δ T Δ New _ D ¯ = New _ D i ¯ + Δ New _ D i ¯ ,
P = [ I | 0 ] P = [ R | t ] q × R q q · q t threshold ,
E rr = i = 1 N ( | K [ R 1 T 1 ] [ M i 1 ] u i | + | K [ R 2 T 2 ] [ M i 1 ] u i | .
K = [ 1000 0 500 0 1200 400 0 0 1 ] .

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