Abstract

Depth from defocus using structured light is a useful optical metrology technique since the camera and projector can be placed on the same optical axis and it can cope with depth discontinuities. However, the technique can suffer from large errors when used on surfaces with differing reflective properties. This paper demonstrates a method for overcoming this problem.

© 2011 Optical Society of America

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References

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  1. A. Medina, F. Gaya, and F. del Pozo, J. Opt. Soc. Am. A 23, 800 (2006).
    [CrossRef]
  2. S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.
  3. Y. Schechner and N. Kiryati, Int. J. Comput. Vis. 39, 141 (2000).
    [CrossRef]
  4. S. Lei and S. Zhang, Opt. Lett. 34, 3080 (2009).
    [CrossRef] [PubMed]

2009 (1)

2006 (2)

A. Medina, F. Gaya, and F. del Pozo, J. Opt. Soc. Am. A 23, 800 (2006).
[CrossRef]

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

2000 (1)

Y. Schechner and N. Kiryati, Int. J. Comput. Vis. 39, 141 (2000).
[CrossRef]

Curless, B.

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

del Pozo, F.

Diebel, J.

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

Gaya, F.

Kiryati, N.

Y. Schechner and N. Kiryati, Int. J. Comput. Vis. 39, 141 (2000).
[CrossRef]

Lei, S.

Medina, A.

Scharstein, D.

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

Schechner, Y.

Y. Schechner and N. Kiryati, Int. J. Comput. Vis. 39, 141 (2000).
[CrossRef]

Seitz, S.

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

Szeliski, R.

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

Zhang, S.

Int. J. Comput. Vis. (1)

Y. Schechner and N. Kiryati, Int. J. Comput. Vis. 39, 141 (2000).
[CrossRef]

J. Opt. Soc. Am. A (1)

Opt. Lett. (1)

Other (1)

S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2006), p. 519.

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Figures (4)

Fig. 1
Fig. 1

Linear transfer function versus distance from the lens for a projector focused at 0.2 m with a f / 2 lens of focal length 100 mm .

Fig. 2
Fig. 2

Model of the bear with no correction demonstrating the color problem.

Fig. 3
Fig. 3

Three-dimensional model of the bear with a texture map added.

Fig. 4
Fig. 4

Corrected depth map of the bear.

Equations (10)

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L p ( x , d ) = B 2 ( 1 + β ( d ) cos ϕ ( x + δ ) ) ,
I b ( x , d ) = g ( x ) B 2 ( 1 + β ( d ) cos ϕ ( x + δ ) ) .
I b ( x , d ) r b B 2 ( 1 + α β ( d ) cos ϕ ( x + δ ) ) ,
I r ( x , d ) = r a a + r b B 2 ( 1 + α β ( d ) cos ϕ ( x + δ ) ) .
I r n ( x , d ) = A + C cos ( ϕ + δ n ) = A + p 1 cos δ n + p 2 sin δ n ,
A = r a a + r b B 2 ,
C = r b B 2 β ( d ) α ,
( A p 1 p 2 ) = ( N cos δ i sin δ i cos δ i cos 2 δ i cos δ i sin δ i sin δ i cos δ i sin δ i sin 2 δ i ) 1 × ( I r n I r n cos δ i I r n sin δ i ) .
V = C A = p 1 2 + p 2 2 A α β ( d ) .
β ( d ) = β V V ,

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