Abstract

This paper describes a method to reconstruct high-speed absolute three-dimensional (3D) geometry using only three encoded 1-bit binary dithered patterns. Because of the use of 1-bit binary patterns, high-speed 3D shape measurement could also be achieved. By matching the right camera image pixel to the left camera pixel in the object space rather than image space, robust correspondence can be established. Experiments demonstrate the robustness of the proposed algorithm and the potential to achieve high-speed 3D shape measurements.

© 2014 Optical Society of America

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References

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  1. X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
    [Crossref]
  2. J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).
  3. D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International journal of computer vision 47, 7–42 (2002).
    [Crossref]
  4. A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Computer Vision–ACCV 2010 (Springer, 2011), pp. 25–38.
    [Crossref]
  5. M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).
  6. P. Heise, S. Klose, B. Jensen, and A. Knoll, “PM-Huber: PatchMatch with Huber Regularization for Stereo Matching,” Computer Vision (ICCV), 2013 IEEE International Conference on pp. 2360–2367 (2013).
    [Crossref]
  7. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002).
    [Crossref]
  8. B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
    [Crossref]
  9. G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
    [Crossref]
  10. X. Zhou and P. Boulanger, “New eye contact correction using radial basis function for wide baseline videoconference system,” in Advances in Multimedia Information Processing–PCM 2012 (Springer, 2012), pp. 68–79.
    [Crossref]
  11. Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).
  12. S. Mattoccia, S. Giardino, and A. Gambini, “Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering,” in Computer Vision–ACCV 2009 (Springer, 2010), pp. 371–380.
    [Crossref]
  13. C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
    [Crossref]
  14. D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps using structured light,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 1, I–195 (2003).
  15. C. Bräuer-Burchardt, P. Kühmstedt, and G. Notni, Combination of Sinusoidal and Single Binary Pattern Projection for Fast 3D Surface Reconstruction (Springer, 2012).
  16. W. Lohry, V. Chen, and S. Zhang, “Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration,” Opt. Express 22, 1287–1301 (2014).
    [Crossref] [PubMed]
  17. Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
    [Crossref]
  18. P. Wissmann, R. Schmitt, and F. Forster, “Fast and accurate 3D scanning using coded phase shifting and high speed pattern projection,” 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on pp. 108–115 (2011).
    [Crossref]
  19. B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
    [Crossref]
  20. H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Trans. Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009).
    [Crossref]
  21. E. A. Nadaraya, “On estimating regression,” Theory of Probability & Its Applications 9, 141–142 (1964).
    [Crossref]
  22. H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2, 807–814 (2005).

2014 (2)

W. Lohry, V. Chen, and S. Zhang, “Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration,” Opt. Express 22, 1287–1301 (2014).
[Crossref] [PubMed]

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

2011 (1)

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).

2009 (1)

H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Trans. Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009).
[Crossref]

2005 (1)

H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2, 807–814 (2005).

2003 (2)

D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps using structured light,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 1, I–195 (2003).

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).

2002 (3)

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International journal of computer vision 47, 7–42 (2002).
[Crossref]

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002).
[Crossref]

2000 (1)

C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
[Crossref]

1999 (1)

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

1998 (1)

G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
[Crossref]

1964 (1)

E. A. Nadaraya, “On estimating regression,” Theory of Probability & Its Applications 9, 141–142 (1964).
[Crossref]

Bleyer, M.

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).

Boulanger, P.

X. Zhou and P. Boulanger, “New eye contact correction using radial basis function for wide baseline videoconference system,” in Advances in Multimedia Information Processing–PCM 2012 (Springer, 2012), pp. 68–79.
[Crossref]

Brauer, W.

G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
[Crossref]

Bräuer-Burchardt, C.

C. Bräuer-Burchardt, P. Kühmstedt, and G. Notni, Combination of Sinusoidal and Single Binary Pattern Projection for Fast 3D Surface Reconstruction (Springer, 2012).

Burges, C. J.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Chen, V.

Comaniciu, D.

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002).
[Crossref]

Dai, J.

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

Davis, J.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).

Feng, T.

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

Forster, F.

P. Wissmann, R. Schmitt, and F. Forster, “Fast and accurate 3D scanning using coded phase shifting and high speed pattern projection,” 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on pp. 108–115 (2011).
[Crossref]

Gambini, A.

S. Mattoccia, S. Giardino, and A. Gambini, “Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering,” in Computer Vision–ACCV 2009 (Springer, 2010), pp. 371–380.
[Crossref]

Geiger, A.

A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Computer Vision–ACCV 2010 (Springer, 2011), pp. 25–38.
[Crossref]

Giardino, S.

S. Mattoccia, S. Giardino, and A. Gambini, “Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering,” in Computer Vision–ACCV 2009 (Springer, 2010), pp. 371–380.
[Crossref]

Heise, P.

P. Heise, S. Klose, B. Jensen, and A. Knoll, “PM-Huber: PatchMatch with Huber Regularization for Stereo Matching,” Computer Vision (ICCV), 2013 IEEE International Conference on pp. 2360–2367 (2013).
[Crossref]

Hirschmuller, H.

H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Trans. Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009).
[Crossref]

H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2, 807–814 (2005).

Hirzinger, G.

G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
[Crossref]

Jensen, B.

P. Heise, S. Klose, B. Jensen, and A. Knoll, “PM-Huber: PatchMatch with Huber Regularization for Stereo Matching,” Computer Vision (ICCV), 2013 IEEE International Conference on pp. 2360–2367 (2013).
[Crossref]

Klose, S.

P. Heise, S. Klose, B. Jensen, and A. Knoll, “PM-Huber: PatchMatch with Huber Regularization for Stereo Matching,” Computer Vision (ICCV), 2013 IEEE International Conference on pp. 2360–2367 (2013).
[Crossref]

Knirsch, P.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Knoll, A.

P. Heise, S. Klose, B. Jensen, and A. Knoll, “PM-Huber: PatchMatch with Huber Regularization for Stereo Matching,” Computer Vision (ICCV), 2013 IEEE International Conference on pp. 2360–2367 (2013).
[Crossref]

Kühmstedt, P.

C. Bräuer-Burchardt, P. Kühmstedt, and G. Notni, Combination of Sinusoidal and Single Binary Pattern Projection for Fast 3D Surface Reconstruction (Springer, 2012).

Li, B.

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

Lohry, W.

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

W. Lohry, V. Chen, and S. Zhang, “Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration,” Opt. Express 22, 1287–1301 (2014).
[Crossref] [PubMed]

Mattoccia, S.

S. Mattoccia, S. Giardino, and A. Gambini, “Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering,” in Computer Vision–ACCV 2009 (Springer, 2010), pp. 371–380.
[Crossref]

Meer, P.

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002).
[Crossref]

Mei, X.

X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
[Crossref]

Mika, S.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Muller, K.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Nadaraya, E. A.

E. A. Nadaraya, “On estimating regression,” Theory of Probability & Its Applications 9, 141–142 (1964).
[Crossref]

Notni, G.

C. Bräuer-Burchardt, P. Kühmstedt, and G. Notni, Combination of Sinusoidal and Single Binary Pattern Projection for Fast 3D Surface Reconstruction (Springer, 2012).

Ramamoorthi, R.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).

Ratsch, G.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Reich, C.

C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
[Crossref]

Rhemann, C.

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).

Ritter, R.

C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
[Crossref]

Roser, M.

A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Computer Vision–ACCV 2010 (Springer, 2011), pp. 25–38.
[Crossref]

Rother, C.

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).

Rusinkiewicz, S.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).

Scharstein, D.

H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Trans. Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009).
[Crossref]

D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps using structured light,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 1, I–195 (2003).

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International journal of computer vision 47, 7–42 (2002).
[Crossref]

Schmitt, R.

P. Wissmann, R. Schmitt, and F. Forster, “Fast and accurate 3D scanning using coded phase shifting and high speed pattern projection,” 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on pp. 108–115 (2011).
[Crossref]

Scholkopf, B.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Shum, H.-Y.

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

Smola, A. J.

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

Sun, X.

X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
[Crossref]

Szeliski, R.

D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps using structured light,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 1, I–195 (2003).

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International journal of computer vision 47, 7–42 (2002).
[Crossref]

Thesing, J.

C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
[Crossref]

Urtasun, R.

A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Computer Vision–ACCV 2010 (Springer, 2011), pp. 25–38.
[Crossref]

Wang, D.

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

Wang, H.

X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
[Crossref]

Wang, J.

Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
[Crossref]

Wang, Y.

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

Wei, G.-Q.

G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
[Crossref]

Wissmann, P.

P. Wissmann, R. Schmitt, and F. Forster, “Fast and accurate 3D scanning using coded phase shifting and high speed pattern projection,” 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on pp. 108–115 (2011).
[Crossref]

Wu, F.

Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
[Crossref]

Xiong, Z.

Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
[Crossref]

Xu, Y.

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

Yang, Z.

Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
[Crossref]

Zhang, S.

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

W. Lohry, V. Chen, and S. Zhang, “Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration,” Opt. Express 22, 1287–1301 (2014).
[Crossref] [PubMed]

Zhang, X.

X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
[Crossref]

Zhang, Y.

Z. Yang, Z. Xiong, Y. Zhang, J. Wang, and F. Wu, “Depth Acquisition from Density Modulated Binary Patterns,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on pp. 25–32 (2013).
[Crossref]

Zhou, M.

X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on pp. 467–474 (2011).
[Crossref]

Zhou, X.

X. Zhou and P. Boulanger, “New eye contact correction using radial basis function for wide baseline videoconference system,” in Advances in Multimedia Information Processing–PCM 2012 (Springer, 2012), pp. 68–79.
[Crossref]

BMVC (1)

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch Stereo-Stereo Matching with Slanted Support Windows,” BMVC 11, 1–11 (2011).

Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on (2)

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth from triangulation,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 2, 359 (2003).

D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps using structured light,” Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on 1, I–195 (2003).

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (1)

H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2, 807–814 (2005).

IEEE Trans. Neural Networks (1)

B. Scholkopf, S. Mika, C. J. Burges, P. Knirsch, K. Muller, G. Ratsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks 10, 1000–1017 (1999).
[Crossref]

IEEE Trans. Pattern Analysis and Machine Intelligence (3)

G.-Q. Wei, W. Brauer, and G. Hirzinger, “Intensity-and gradient-based stereo matching using hierarchical Gaussian basis functions,” IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1143–1160 (1998).
[Crossref]

D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002).
[Crossref]

H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Trans. Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009).
[Crossref]

International journal of computer vision (1)

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International journal of computer vision 47, 7–42 (2002).
[Crossref]

Opt. Express (1)

Opt. Laser Eng. (1)

B. Li, Y. Wang, J. Dai, W. Lohry, and S. Zhang, “Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques,” Opt. Laser Eng. 54, 236–246 (2014), (invited); doi: .
[Crossref]

Optical Engineering (1)

C. Reich, R. Ritter, and J. Thesing, “3-D shape measurement of complex objects by combining photogrammetry and fringe projection,” Optical Engineering 39, 224–231 (2000).
[Crossref]

Pattern Recognition, 2002. Proceedings. 16th International Conference on (1)

Y. Xu, D. Wang, T. Feng, and H.-Y. Shum, “Stereo computation using radial adaptive windows,” Pattern Recognition, 2002. Proceedings. 16th International Conference on 3, 595–598 (2002).

Theory of Probability & Its Applications (1)

E. A. Nadaraya, “On estimating regression,” Theory of Probability & Its Applications 9, 141–142 (1964).
[Crossref]

Other (8)

S. Mattoccia, S. Giardino, and A. Gambini, “Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering,” in Computer Vision–ACCV 2009 (Springer, 2010), pp. 371–380.
[Crossref]

C. Bräuer-Burchardt, P. Kühmstedt, and G. Notni, Combination of Sinusoidal and Single Binary Pattern Projection for Fast 3D Surface Reconstruction (Springer, 2012).

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Supplementary Material (5)

» Media 1: MP4 (546 KB)     
» Media 2: MP4 (4230 KB)     
» Media 3: MP4 (5350 KB)     
» Media 4: MP4 (2258 KB)     
» Media 5: MP4 (2838 KB)     

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Figures (8)

Fig. 1
Fig. 1 Example of the 8-bit grayscale patterns and the resultant dithered patterns. (a) Random pattern; (b) 8-bit sinusoidal fringe pattern; (c) Modified sinusoidal fringe pattern by combining (a) and (b) using Eq. (2); (d) Final 1-bit dithered patter after applying the random dithering technique.
Fig. 2
Fig. 2 Radial basis function (RBF) used to remap coded image to a reference phase with ε = 10/rad.
Fig. 3
Fig. 3 The encoded pattern from the right camera is compared to the left camera either without remapping (top row) or with remapping (bottom row). Using the traditional sliding window approach causes ”rings” of low cost in regions of large disparity variation. By applying remapping (bottom row), the cost is minimized throughout the entire region, reducing the possibility of a false match. A video of the remapping technique applied to multiple disparity levels is shown in Media 1.
Fig. 4
Fig. 4 Comparing results of using phase remapping and coded texture images. Media 2 shows the animation of the costs for all disparities using the proposed method, and Media 3 shows the animation of the costs for all disparities using SAD. (a)–(c) Costs for disparities of 35, 45, and 55 pixels using phase remapping; (d)–(f) Corresponding results of (a)–(c) by directly using the coded images.
Fig. 5
Fig. 5 Experimental results of measuring two separate statues. (a) System setup; (b) Coded pattern from left camera; (c) Coded pattern from right camera; (d) Phase map from left camera; (e) Phase map from right camera; (f) Remapped cost aggregation; (g) Result of (f) after applying dynamic programming; (h) Result of (g) after applying median filtering.
Fig. 6
Fig. 6 Typical 3D frames of capturing a moving hand at 50 Hz (refer to Media 4 for the complete video sequence). (a) Time (0 s); (b) Time (0.25 s); (c) Time (0.50 s); and (d) Time (0.75 s).
Fig. 7
Fig. 7 Typical 3D frames of capturing a moving face at 50 Hz (refer to Media 5 for the complete video sequence).
Fig. 8
Fig. 8 Comparison of errors using a reference plane and using the proposed method. (a) phase error from the dithered pattern; (b) depth error using the dithered pattern and a reference plane; (c) depth error using the proposed technique.

Equations (10)

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I k ( x , y ) = I ( x , y ) + I ( x , y ) cos [ ϕ ( x , y ) 2 π k 3 ] .
I k ( x , y ) = I ( x , y ) + I p ( x , y ) I ( x , y ) cos [ ϕ ( x , y ) 2 π k 3 ] .
γ ( x , y ) = I ( x , y ) I ( x , y ) = 3 ( I 1 I 3 ) 2 + ( 2 I 2 I 1 I 3 ) 2 I 1 + I 2 + I 3
I k d ( x , y ) = { 1 if I k ( x , y ) > Unif ( 0 , 1 ) 0 otherwise .
I ^ R ( x d , y ) = i = N N w i ( x , y , d ) I R ( x d + i , y ) i = N N w i ( x , y , d ) ,
w i ( x , y , d ) = γ K ε ( ϕ L ( x , y ) , ϕ R ( x d + i , y ) ) .
γ ( x , y ) = { t if γ ( x , y ) t γ ( x , y ) otherwise .
K ε ( ϕ L , ϕ R ) = 1 1 + ( ε r ) 2 ,
SAD ( x , y , d ) = i = N N j = N N | I L ( x + i , y + j ) I ^ R ( x d + i , y + j ) | .
L r ( p , d ) = C ( p , d ) + min [ L r ( p r , d ) , L r ( p r , d 1 ) + P 1 , L r ( p r , d + 1 ) + P 1 , min i L r ( p r , i ) + P 2 ]

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