Abstract

Fringe projection is an extensively used technique for high speed three-dimensional (3-D) measurements of dynamic objects. To precisely retrieve a moving object at pixel level, researchers prefer to project a sequence of fringe images onto its surface. However, the motion often leads to artifacts in reconstructions due to the sequential recording of the set of patterns. In order to reduce the adverse impact of the movement, we present a novel high speed 3-D scanning technique combining the fringe projection and stereo. Firstly, promising measuring speed is achieved by modifying the traditional aperiodic sinusoidal patterns so that the fringe images can be cast at kilohertz with the widely used defocusing strategy. Next, a temporal intensity tracing algorithm is developed to further alleviate the influence of motion by accurately tracing the ideal intensity for stereo matching. Then, a combined cost measure is suggested to robustly estimate the cost for each pixel and lastly a three-step framework of refinement follows not only to eliminate outliers caused by the motion but also to obtain sub-pixel disparity results for 3-D reconstructions. In comparison with the traditional method where the effect of motion is not considered, experimental results show that the reconstruction accuracy for dynamic objects can be improved by an order of magnitude with the proposed method.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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2017 (1)

S. Feng, Q. Chen, C. Zuo, and A. Asundi, “Fast three-dimensional measurements for dynamic scenes with shiny surfaces,” Opt. Commun. 382, 18–27 (2017).
[Crossref]

2016 (5)

T. Tao, Q. Chen, J. Da, S. Feng, Y. Hu, and C. Zuo, “Real-time 3-d shape measurement with composite phase-shifting fringes and multi-view system,” Opt. Express 24, 20253–20269 (2016).
[Crossref] [PubMed]

S. Van der Jeught and J. J. J. Dirckx, “Real-time structured light profilometry: a review,” Opt. Lasers Eng. 87, 18–31 (2016).
[Crossref]

C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
[Crossref]

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2016).
[Crossref]

2015 (2)

S. Feng, Q. Chen, and C. Zuo, “Graphics processing unit-assisted real-time three-dimensional measurement using speckle-embedded fringe,” Appl. Opt. 54, 6865–6873 (2015).
[Crossref] [PubMed]

P. Cong, Z. Xiong, Y. Zhang, S. Zhao, and F. Wu, “Accurate dynamic 3d sensing with fourier-assisted phase shifting,” IEEE J. Sel. Topics Signal Process. 9, 396–408 (2015).
[Crossref]

2014 (2)

B. Harendt, M. Große, M. Schaffer, and R. Kowarschik, “3d shape measurement of static and moving objects with adaptive spatiotemporal correlation,” Appl. Opt. 53, 7507–7515 (2014).
[Crossref] [PubMed]

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

2013 (1)

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

2012 (2)

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20, 19493–19510 (2012).
[Crossref] [PubMed]

Q. Zhang, X. Su, L. Xiang, and X. Sun, “3-d shape measurement based on complementary gray-code light,” Opt. Lasers Eng. 50, 574–579 (2012).
[Crossref]

2011 (2)

M. Schaffer, M. Große, B. Harendt, and R. Kowarschik, “High-speed optical 3-d measurements for shape representation,” Opt. Photonics News 22, 49 (2011).
[Crossref]

Y. Wang and S. Zhang, “Superfast multifrequency phase-shifting technique with optimal pulse width modulation,” Opt. Express 19, 5149–5155 (2011).
[Crossref] [PubMed]

2010 (2)

2009 (1)

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, “Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 492–504 (2009).
[Crossref] [PubMed]

2007 (1)

2006 (1)

2005 (2)

2004 (1)

J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[Crossref]

2003 (1)

2002 (1)

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vision 47, 7–42 (2002).
[Crossref]

1994 (1)

J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” Int. J. Comput. Vision 12, 43–77 (1994).
[Crossref]

Asundi, A.

S. Feng, Q. Chen, C. Zuo, and A. Asundi, “Fast three-dimensional measurements for dynamic scenes with shiny surfaces,” Opt. Commun. 382, 18–27 (2017).
[Crossref]

C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
[Crossref]

Barron, J. L.

J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” Int. J. Comput. Vision 12, 43–77 (1994).
[Crossref]

Batlle, J.

J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[Crossref]

Beauchemin, S. S.

J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” Int. J. Comput. Vision 12, 43–77 (1994).
[Crossref]

Bouguet, J.-Y.

J.-Y. Bouguet, “Camera calibration toolbox for matlab,” (2004).

Carter, J. N.

T. Monks and J. N. Carter, “Improved stripe matching for colour encoded structured light,” in Proceedings International Conference on Computer Analysis of Images and Patterns (Springer), pp. 476–485.

Chen, Q.

S. Feng, Q. Chen, C. Zuo, and A. Asundi, “Fast three-dimensional measurements for dynamic scenes with shiny surfaces,” Opt. Commun. 382, 18–27 (2017).
[Crossref]

C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
[Crossref]

T. Tao, Q. Chen, J. Da, S. Feng, Y. Hu, and C. Zuo, “Real-time 3-d shape measurement with composite phase-shifting fringes and multi-view system,” Opt. Express 24, 20253–20269 (2016).
[Crossref] [PubMed]

S. Feng, Q. Chen, and C. Zuo, “Graphics processing unit-assisted real-time three-dimensional measurement using speckle-embedded fringe,” Appl. Opt. 54, 6865–6873 (2015).
[Crossref] [PubMed]

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20, 19493–19510 (2012).
[Crossref] [PubMed]

Chicharo, J. F.

Cong, P.

P. Cong, Z. Xiong, Y. Zhang, S. Zhao, and F. Wu, “Accurate dynamic 3d sensing with fourier-assisted phase shifting,” IEEE J. Sel. Topics Signal Process. 9, 396–408 (2015).
[Crossref]

Curless, B.

Z. Li, B. Curless, and S. M. Seitz, “Spacetime stereo: shape recovery for dynamic scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 367–374.

Da, J.

Davis, J.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: a unifying framework for depth from triangulation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 359–366.

Dietrich, P.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

Dirckx, J. J. J.

S. Van der Jeught and J. J. J. Dirckx, “Real-time structured light profilometry: a review,” Opt. Lasers Eng. 87, 18–31 (2016).
[Crossref]

Feng, F.

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20, 19493–19510 (2012).
[Crossref] [PubMed]

Feng, S.

Fleet, D. J.

J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” Int. J. Comput. Vision 12, 43–77 (1994).
[Crossref]

Gool, L. V.

T. Weise, B. Leibe, and L. V. Gool, “Fast 3d scanning with automatic motion compensation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Große, M.

B. Harendt, M. Große, M. Schaffer, and R. Kowarschik, “3d shape measurement of static and moving objects with adaptive spatiotemporal correlation,” Appl. Opt. 53, 7507–7515 (2014).
[Crossref] [PubMed]

M. Schaffer, M. Große, B. Harendt, and R. Kowarschik, “High-speed optical 3-d measurements for shape representation,” Opt. Photonics News 22, 49 (2011).
[Crossref]

Grosse, M.

Gu, G.

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20, 19493–19510 (2012).
[Crossref] [PubMed]

Gu, Y.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2016).
[Crossref]

Guan, C.

Hall-Holt, O.

O. Hall-Holt and S. Rusinkiewicz, “Stripe boundary codes for real-time structured-light range scanning of moving objects,” in Proceedings of IEEE International Conference on Computer Vision (IEEE2001), pp. 359–366 vol.2.

Harendt, B.

B. Harendt, M. Große, M. Schaffer, and R. Kowarschik, “3d shape measurement of static and moving objects with adaptive spatiotemporal correlation,” Appl. Opt. 53, 7507–7515 (2014).
[Crossref] [PubMed]

M. Schaffer, M. Große, B. Harendt, and R. Kowarschik, “High-speed optical 3-d measurements for shape representation,” Opt. Photonics News 22, 49 (2011).
[Crossref]

Hartley, R.

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2003).

Hassebrook, L. G.

Heist, S.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

Hu, K.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2016).
[Crossref]

Hu, Y.

Huang, K.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2016).
[Crossref]

Huang, L.

C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
[Crossref]

Huertas, A.

A. N. Stein, A. Huertas, and L. Matthies, “Attenuating stereo pixel-locking via affine window adaptation,” in Proceedings of IEEE Conference on Robotics and Automation (ICRA, 2006), pp. 914–921.

Jiao, S.

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

Kowarschik, R.

Kühmstedt, P.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

Lau, D. L.

Leibe, B.

T. Weise, B. Leibe, and L. V. Gool, “Fast 3d scanning with automatic motion compensation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Li, E. B.

Li, R.

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

Li, Z.

Z. Li, B. Curless, and S. M. Seitz, “Spacetime stereo: shape recovery for dynamic scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 367–374.

Lutzke, P.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

Mann, A.

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

Matthies, L.

A. N. Stein, A. Huertas, and L. Matthies, “Attenuating stereo pixel-locking via affine window adaptation,” in Proceedings of IEEE Conference on Robotics and Automation (ICRA, 2006), pp. 914–921.

Mei, X.

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

Monks, T.

T. Monks and J. N. Carter, “Improved stripe matching for colour encoded structured light,” in Proceedings International Conference on Computer Analysis of Images and Patterns (Springer), pp. 476–485.

Nistér, D.

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, “Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 492–504 (2009).
[Crossref] [PubMed]

Notni, G.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

Oliver, J.

Pagès, J.

J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[Crossref]

Peng, X.

Qu, Y.

Ramamoorthi, R.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: a unifying framework for depth from triangulation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 359–366.

Rusinkiewicz, S.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: a unifying framework for depth from triangulation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 359–366.

O. Hall-Holt and S. Rusinkiewicz, “Stripe boundary codes for real-time structured-light range scanning of moving objects,” in Proceedings of IEEE International Conference on Computer Vision (IEEE2001), pp. 359–366 vol.2.

Salvi, J.

J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[Crossref]

Schaffer, M.

Scharstein, D.

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vision 47, 7–42 (2002).
[Crossref]

Schmidt, I.

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

Schreiber, P.

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
[Crossref]

Seitz, S. M.

Z. Li, B. Curless, and S. M. Seitz, “Spacetime stereo: shape recovery for dynamic scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 367–374.

Shen, G.

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
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S. Van der Jeught and J. J. J. Dirckx, “Real-time structured light profilometry: a review,” Opt. Lasers Eng. 87, 18–31 (2016).
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S. Feng, Q. Chen, C. Zuo, and A. Asundi, “Fast three-dimensional measurements for dynamic scenes with shiny surfaces,” Opt. Commun. 382, 18–27 (2017).
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C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
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T. Tao, Q. Chen, J. Da, S. Feng, Y. Hu, and C. Zuo, “Real-time 3-d shape measurement with composite phase-shifting fringes and multi-view system,” Opt. Express 24, 20253–20269 (2016).
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C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20, 19493–19510 (2012).
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Appl. Opt. (4)

IEEE J. Sel. Topics Signal Process. (1)

P. Cong, Z. Xiong, Y. Zhang, S. Zhao, and F. Wu, “Accurate dynamic 3d sensing with fourier-assisted phase shifting,” IEEE J. Sel. Topics Signal Process. 9, 396–408 (2015).
[Crossref]

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

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2016).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, “Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 492–504 (2009).
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D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vision 47, 7–42 (2002).
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Opt. Commun. (1)

S. Feng, Q. Chen, C. Zuo, and A. Asundi, “Fast three-dimensional measurements for dynamic scenes with shiny surfaces,” Opt. Commun. 382, 18–27 (2017).
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Opt. Eng. (1)

S. Heist, A. Mann, P. Kühmstedt, P. Schreiber, and G. Notni, “Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement,” Opt. Eng. 53, 112208 (2014).
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Opt. Express (8)

Opt. Lasers Eng. (5)

C. Zuo, Q. Chen, G. Gu, S. Feng, F. Feng, R. Li, and G. Shen, “High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection,” Opt. Lasers Eng. 51, 953–960 (2013).
[Crossref]

C. Zuo, L. Huang, M. Zhang, Q. Chen, and A. Asundi, “Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review,” Opt. Lasers Eng. 85, 84–103 (2016).
[Crossref]

S. Van der Jeught and J. J. J. Dirckx, “Real-time structured light profilometry: a review,” Opt. Lasers Eng. 87, 18–31 (2016).
[Crossref]

S. Heist, P. Lutzke, I. Schmidt, P. Dietrich, P. Kühmstedt, A. Tünnermann, and G. Notni, “High-speed three-dimensional shape measurement using gobo projection,” Opt. Lasers Eng. 87, 90–96 (2016).
[Crossref]

Q. Zhang, X. Su, L. Xiang, and X. Sun, “3-d shape measurement based on complementary gray-code light,” Opt. Lasers Eng. 50, 574–579 (2012).
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Opt. Photonics News (1)

M. Schaffer, M. Große, B. Harendt, and R. Kowarschik, “High-speed optical 3-d measurements for shape representation,” Opt. Photonics News 22, 49 (2011).
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Pattern Recogn. (1)

J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
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Other (9)

T. Monks and J. N. Carter, “Improved stripe matching for colour encoded structured light,” in Proceedings International Conference on Computer Analysis of Images and Patterns (Springer), pp. 476–485.

T. Weise, B. Leibe, and L. V. Gool, “Fast 3d scanning with automatic motion compensation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

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Z. Li, B. Curless, and S. M. Seitz, “Spacetime stereo: shape recovery for dynamic scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 367–374.

J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: a unifying framework for depth from triangulation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 359–366.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and Z. Xiaopeng, “On building an accurate stereo matching system on graphics hardware,” in Proceedings of IEEE Computer Vision Workshops (ICCV Workshops) (IEEE, 2011), pp. 467–474.
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R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2003).

J.-Y. Bouguet, “Camera calibration toolbox for matlab,” (2004).

A. N. Stein, A. Huertas, and L. Matthies, “Attenuating stereo pixel-locking via affine window adaptation,” in Proceedings of IEEE Conference on Robotics and Automation (ICRA, 2006), pp. 914–921.

Supplementary Material (2)

NameDescription
» Visualization 1: MP4 (864 KB)      Visualization 1
» Visualization 2: MP4 (3371 KB)      Visualization 2

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Figures (14)

Fig. 1
Fig. 1 (a) Temporal sequence of captured intensities from a static object; (b) Temporal sequence of captured intensities from a moving object.
Fig. 2
Fig. 2 Schematic of the intensity tracing algorithm.
Fig. 3
Fig. 3 Refinement of the measured correspondence (eLeft and eRight are the epipoles).
Fig. 4
Fig. 4 The search for optimum points and d. (a) Left view; (b) Right view.
Fig. 5
Fig. 5 Reconstruction error of a static plane. (a) TNCC; (b) Our method.
Fig. 6
Fig. 6 Measurement of a couple of gage balls. (a) The gage balls; (b) 3-D reconstruction.
Fig. 7
Fig. 7 Illustration of the moving process of the plane.
Fig. 8
Fig. 8 (a) Selected three positions of the plane; (b) Reconstruction error of the TNCC for each position; (c) Reconstruction error of our method for each position.
Fig. 9
Fig. 9 Measurement results obtained with the window size of 5. (a) Captured fringe images for positions P1 to P3; (b) 3-D reconstructions of the three positions obtained by TNCC; (c) 3-D reconstructions of the three positions obtained by our method; (d) Reconstruction error of our method for the 320th column of the plane.
Fig. 10
Fig. 10 Demonstration of the practical intensity tracing procedure. (a) The result of pixel (268,177); (b) The result of pixel (458,377).
Fig. 11
Fig. 11 (a) Fringe patterns of a swinging table tennis ball captured at different times; (b) Corresponding 3-D reconstructions.
Fig. 12
Fig. 12 (a) A captured fringe pattern of a motionless plaster model; (b) Calculated disparity map of the model; (c) The reconstructed depth; (d) A captured fringe image of a complex static scene; (e) Obtained disparity map of the scene; (f) The reconstructed depth.
Fig. 13
Fig. 13 High speed 3-D measurement of a moving hand (see Visualization 1).
Fig. 14
Fig. 14 High speed 3-D measurement of a deflating toy balloon (see Visualization 2).

Tables (2)

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Table 1 Parameter Settings

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Table 2 Measurement Results of the Swinging Ball

Equations (35)

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I i ( x , y ) = a i ( x , y ) + b i ( x , y ) sin [ c i ( x , y ) x + e i ( x , y ) ] 0
S j ( x , y ) = { 1 0 x < h j 0 h j x < g j
I i ( x , y ) = U [ S i 1 , S i 2 , S i 3 , , S i j ]
C AD ( p , d ) = | I Left ( p ) I Right ( p d ) |
C gradAD ( p , d ) = | I grad Left ( p ) I grad Right ( p d ) |
C NCC ( p , d ) = 1 | t [ Q Left ( t ) Q ¯ Left ] [ Q Right ( t ) Q ¯ Right ] t [ Q Left ( t ) Q ¯ Left ] 2 t [ Q Right ( t ) Q ¯ Right ] 2 |
C census ( p , d ) = HamDist [ I cen Left ( p ) , I cen Right ( p d ) ]
C ( p , d ) = w AD C AD ( p , d ) + w gradAD C gradAD ( p , d ) + w NCC C NCC ( p , d ) + w census C census ( p , d ) w AD + w gradAD + w NCC + w census
d ( t ) = t k + d 0
C ( p , d ) = min k C ( p , d ( t ) )
d 1 = arg min { d | d min d d max } C ( p , d )
d 2 = arg min { d | d min d d max , d d 1 } C ( p , d )
d 3 = arg min { d | d min d d max , d d 1 , d d 2 } C ( p , d )
d N = arg min { d | d min d d max , d d 1 , d d 2 , , d d N 1 } C ( p , d )
C Nb ( d n ) = C AD W ( d n ) + C gradAD W ( d n ) + C NCC W ( d n ) 3
C AD W ( d n ) = x = r N x = + r N | W Left ( x ) W Right ( x d n ) | ( 2 r N + 1 ) η 1
C gradAD W ( d n ) = x = r N x = + r N | W grad Left ( x ) W grad Right ( x d n ) | ( 2 r N + 1 ) η 2
C NCC W ( d n ) = 1 | x = r N x = + r N [ W Left ( x ) W ¯ Left ( x d n ) ] [ W Right ( x ) W ¯ Right ( x d n ) ] x = r N x = + r N [ W Left ( x ) W ¯ Left ( x d n ) ] 2 x = r N x = + r N [ W Right ( x ) W ¯ Right ( x d n ) ] 2 |
d = arg min d n { d 1 , d 2 . . , d N } C Nb ( d n )
( x , y ) { I 0 ( x , y ) < α I 2 ( x , y ) < α I T 1 ( x , y ) < α }
( x , y ) { | D Left ( x , y ) D Right ( x D Left ( x , y ) , y ) | > β }
D Left ( x , y ) = D Right ( x D Left ( x , y ) , y )
w r ( p , p ) = exp [ ( ( p x p x ) 2 + ( p y p y ) 2 γ d + | I ( p ) I ( p ) | γ i ) ]
d = d ref + d off
d off = flow v x
flow = L K [ I Left ( x , y ) , I Right ( x d r , y ) ]
{ p = H Left P p d = H Right P
C dist ( p , p d ) = dist ( p , p ^ ) 2 + dist ( p , p ^ d ) 2 subjectto p ^ d T F p ^ = 0
C dist ( p , p d ) = dist ( p , l ) 2 + dist ( p , l ) 2
F = ( F 4 f f F 3 f F 4 f F 2 f F 1 F 2 F 4 f F 3 F 4 )
dist ( p , l ) 2 = v 2 1 + ( v f ) 2
l ( v ) = F ( 0 , v , 1 ) T = [ f ( F 3 v + F 4 ) , F 1 v + F 2 , F 3 v + F 4 ] T
dist ( p d , l ) 2 = ( F 3 v + F 4 ) 2 ( F 1 v + F 2 ) 2 + f 2 ( F 3 v + F 4 ) 2
C dist ( v ) = v 2 1 + ( v f ) 2 + ( F 3 v + F 4 ) 2 ( F 1 v + F 2 ) 2 + f 2 ( F 3 v + F 4 ) 2
{ p ^ = H Left P ^ p ^ d = H Right P ^

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