Abstract

Based on recent discoveries, we introduce a method to project a single structured pattern onto an object and then reconstruct the three-dimensional range from the distortions in the reflected and captured image. Traditional structured light methods require several different patterns to recover the depth, without ambiguity or albedo sensitivity, and are corrupted by object movement during the projection/capture process. Our method efficiently combines multiple patterns into a single composite pattern projection allowing for real-time implementations. Because structured light techniques require standard image capture and projection technology, unlike time of arrival techniques, they are relatively low cost.

© 2002 Optical Society of America

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References

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Appl. Opt.

Artif. Intell.

P. M. Will and K. S. Pennington, �??Grid coding: A preprocessing technique for robot and machine vision,�?? Artif. Intell. 2, 319�??329 (1971).
[CrossRef]

Comput. Vision Graph. Image Process

J. L. Posdamer and M. D. Altschuler, �??Surface measurement by space-encoded projected beam systems,�?? Comput. Vision Graph. Image Process. 18, 1�??17 (1982).
[CrossRef]

B. Carrihill and R. Hummel, �??Experiments with intensity ratio depth sensor,�?? Comput. Vision Graph. Image Process. 32, 337�??358 (1985).
[CrossRef]

IBM Technical Disclosure Bulletin

D. S. Goodman and L. G. Hassebrook, �??Face recognition under varying pose,�?? IBM Technical Disclosure Bulletin 27, 2671�??2673 (1984).

IEEE Trans. Pattern. Anal. Mach. Intell.

M. Maruyama and S. Abe, �??Range sensing by projecting multiple slits with random cuts,�?? IEEE Trans. Pattern. Anal. Mach. Intell. 15, 647�??651 (1993).
[CrossRef]

K. Boyer and A. Kak, �??Color-encoded structured light for rapid active ranging,�?? IEEE Trans. Pattern. Anal. Mach. Intell. 9, 2724�??2729 (1991).

J. Opt. Soc. Am. A

Naturwiss

G. Schmaltz of Schmaltz Brothers Laboratories, �??A method for presenting the profile curves of rough surfaces,�?? Naturwiss 18, 315�??316 (1932).
[CrossRef]

Opt. Eng.

F. Chen, G. M. Brown, and M. Song, �??Overview of three-dimensional shape measurement using optical methods,�?? Opt. Eng. 39, 10�??22 (2000).
[CrossRef]

Opt. Lasers Eng.

T. R. Judge and P. J. Bryanston-Cross, �??A review of phase unwrapping techniques in fringe analysis,�?? Opt. Lasers Eng. 21, 199�??239 (1994).
[CrossRef]

Pattern Recogn.

J. Batlle, E. Mouaddib, and J. Salvi, �??Recent progress in coded structured light as a technique to solve the correspondence problem: A survey,�?? Pattern Recogn. 31, 963�??982 (1998)
[CrossRef]

Proc. SPIE

L. G. Hassebrook, R. C. Daley, and W. Chimitt, �??Application of communication theory to high speed structured light illumination,�?? in SPIE Proceedings, Harding and Svetko., eds., Proc. 3204(15), 102�??113 (1997)

Jielin Li and L. G. Hassebrook, �??A robust svd based calibration of active range sensors,�?? in SPIE Proceedings on Visual Information Processing IX, S. K. Park and Z. Rahman, eds., (2000).

Other

G. Goli, C. Guan, L. G. Hassebrook, and D. L. Lau, �??Video rate three dimensional data acquisition using composite light structure pattern,�?? Tech. Rep. CSP 02-002, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY USA (2002).

Supplementary Material (4)

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Figures (10)

Fig. 1.
Fig. 1.

Geometrical representation of the experimental Setup.

Fig. 2.
Fig. 2.

A composite pattern (CP) is formed by modulating traditional PMP patterns along the orthogonal direction.

Fig. 3.
Fig. 3.

Illustration of the spectrum of the captured image for the four channel composite pattern projection.

Fig. 4.
Fig. 4.

Block diagram of the decoding process.

Fig. 5.
Fig. 5.

Depth reconstruction of a single depth step with circle shape. (a) Captured image of the reference plane. (b) Phase map of the reference plane. (c) Captured image of the object plane. (d) Phase map of the object plane. (e) Reconstructed depth of the object scene.

Fig. 6.
Fig. 6.

3D reconstruction with the present of albedo variation. (a) Captured object image with dark circle at the center. (b) Range image representation of reconstructed 3D surface. (c) Reconstructed 3D object surface. (d) The “eye diagram” generated at line y=200 from the depth map for nine shifts of the composite pattern along the orthogonal direction.

Fig. 7.
Fig. 7.

(1.0 Mb) Movie clip showing real-time depth reconstruction for the subject tossing a octahedron.

Fig. 8.
Fig. 8.

(1.0 Mb) Movie clip showing real-time depth reconstruction for the subject stretching out her hands.

Fig. 9.
Fig. 9.

(0.6 Mb) Movie clip showing real-time depth reconstruction for humancomputer interfacing.

Fig. 10.
Fig. 10.

(1.8 Mb) Movie clip showing hand control of a virtual reality viewpoint.

Equations (17)

Equations on this page are rendered with MathJax. Learn more.

I n p ( x p , y p ) = A p + B p cos ( 2 π f ϕ y p 2 πn N ) ,
I n ( x , y ) = α x y · [ A + B cos ( 2 π f ϕ y p + ϕ x y 2 πn N ) ]
ϕ x y = arctan [ Σ n = 1 N I n x y sin ( 2 πn N ) Σ n = 1 N I n x y cos ( 2 πn N ) ] .
h = BC ¯ · ( L d ) 1 + BC ¯ d ,
BC ¯ = β ( ϕ C ϕ B ) .
I n p = c + cos ( 2 π f ϕ y p 2 πn N ) .
I p = A p + B p · n = 1 N I n p · cos ( 2 π f n p x p )
A p = I min B p · min { n = 1 N I n p · cos ( 2 π f n p x p ) }
B p = ( I max I min ) ( max { n = 1 N I n p · cos ( 2 π f n x p ) } min { n = 1 N I n p · cos ( 2 π f n x p ) } )
I CP x y = α x y { A + B · n = 1 N I n x y · cos ( 2 π f n x ) }
I n x y = c + cos ( 2 π f ϕ y p + ϕ x y 2 πn N ) ,
f n c = 1 2 ( f n 1 + f n )
I n BP x y = I CP x y h BP n I n x y · cos ( 2 π f n x )
I n BP x y = I n x y · cos ( 2 π ( f n + δ f ) x + δ θ )
( I n BP x y ) 2 = ( I n x y ) 2 · 1 + cos ( 4 π ( f n + δ f ) x + 2 δ θ ) 2 .
g x y = ( I n BP x y ) 2 h LP ( x ) = ( I n x y ) 2 2 .
I n R x y = 2 g x y = 2 · [ ( I n BP x y ) 2 h LP ( x ) ] .

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