This Article 
 Bibliographic References 
 Add to: 
Real-Time Range Acquisition by Adaptive Structured Light
March 2006 (vol. 28 no. 3)
pp. 432-445
The goal of this paper is to provide a "self-adaptive” system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues—based upon pattern color, geometry, and tracking—yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity.

[1] F. Blais, “Review of 20 Years of Range Sensor Development,” J. Electronic Imaging, vol. 13, no. 1, pp. 231-240, Jan. 2004.
[2] D. Scharstein and R. Szeliski, “High-Accuracy Stereo Depth Maps Using Structured Light,” Proc. IEEE CS Conf. Computer Vision pp. 195-202, 2003.
[3] Z. Zhang, R. Deriche, O. Faugeras, and Q. Luong, “A Robust Technique for Matching Two Uncalibrated Images through the Recovery of the Unknown Epipolar Geometry,” Artificial Intelligence J., vol. 78, nos. 1-2, pp. 87-119, Oct. 1995.
[4] J. Forest and J. Salvi, “A Review of Laser Scanning Three-Dimensional Digitisers,” Proc. IEEE/RJS Int'l Conf. Intelligent Robots and Systems, pp. 73-78, 2002.
[5] F. Blais, M. Picard, and G. Godin, “Recursive Model Optimization Using ICP and Free Moving 3D Data Acquisition,” Proc. Fourth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 251-259, 2003.
[6] J. Batlle, E. Mouaddib, and J. Salvi, “Recent Progress in Coded Structured Light as a Technique to Solve the Correspondence Problem: A Survey,” Pattern Recognition, vol. 31, no. 7, pp. 963-982, 1998.
[7] S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-Time 3D Model Acquisition,” Proc. SIGGRAPH, pp. 438-446, 2002.
[8] M.D. Altschuler, B.R. Altschuler, J. Dijaki, L.A. Tamburino, and B. Woolford, “Robot Vision by Encoded Light Beams,” Three-Dimensional Machine Vision, T. Kanade, ed., vol 87, pp. 97-149. Kluwer Academic, 1987.
[9] K. Boyer and A. Kak, “Color-Encoded Structured Light for Rapid Active Ranging,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 1, pp. 14-28, Jan. 1987.
[10] M. Proesmans and L. Van Gool, “One-Shot Active 3D Image Capture,” Proc. SPIE, vol. 3023, Three-Dimensional Image Capture, pp. 50-61, 1997.
[11] D. Caspi, N. Kyriati, and J. Shamir, “Range Imaging With Adaptive Color Structured Light,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 470-480, May 1998.
[12] C. Je, S. Lee, and R. Park, “High-Contrast Color Stripe Pattern for Rapid Structured-Light Range Imaging,” Proc. Eighth European Conf. Comp. Vision, pp. 95-107, 2004.
[13] M. Maruyama and S. Abe, “Range Sensing by Projecting Multiple Slits with Random Cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 6, pp. 647-651, June 1993.
[14] P. Vuylsteke and A. Oosterlinck, “Range Image Acquisition with a Single Binary-Encoded Light Pattern,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 2, pp. 148-164, Feb. 1990.
[15] P. Griffin, L. Narasimhan, and S. Yee, “Generation of Uniquely Encoded Light Patterns for Range Data Acquisition,” Pattern Recognition, vol. 25, no. 6, pp. 609-616, 1992.
[16] E. Horn and N. Kiryati, ”Toward Optimal Structured Light Patterns,” Image and Vision Computing, vol. 17, pp. 87-97, 1999.
[17] L. Zhang, B. Curless, and S. Seitz, ”Rapid Shape Acquisition Using Color Structured Light and Multi-Pass Dynamic Programming,” Proc. First Int'l Symp. 3D Data Processing Visualization and Transmission, pp. 24-36, 2002.
[18] A. Strat and M. Oliveira, “A Point-and-Shoot Color 3D Camera,” Proc. Fourth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 483-490, 2003.
[19] L. Zhang, N. Snavely, B. Curless, and S. Seitz, “Spacetime Faces: High-Resolution Capture for Modeling and Animation,” Proc. ACM Ann. Conf. Computer Graphics, pp. 548-558, 2004.
[20] Y. Boykov and V. Kolmogorov, “An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1138-1154, Sept. 2004.
[21] T. Koninckx and L. Van Gool, “High-Speed Active 3D Acquisition Based in a Pattern-Specific Mesh,” Proc. SPIE: EI Photometrics, pp. 26-37, 2003.
[22] T. Koninckx, A. Griesser, and L. Van Gool, “Real-Time Range Scanning of Deformable Surfaces by Adaptively Coded Structured Light,” Proc. Fourth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 293-302, 2003.
[23] T. Jaeggli, T.P. Koninckx, and L. Van Gool, “Online 3D Acquisition and Model Integration,” Proc. IEEE Int'l Workshop Projector-Camera Systems, 2003.
[24] T.P. Koninckx, “Adaptive Structured Light,” PhD thesis, Catholic Univ. Leuven, 2005, .

Index Terms:
Index Terms- Imaging geometry, depth cues, range data, shape, real-time systems.
Thomas P. Koninckx, Luc Van Gool, "Real-Time Range Acquisition by Adaptive Structured Light," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 432-445, March 2006, doi:10.1109/TPAMI.2006.62
Usage of this product signifies your acceptance of the Terms of Use.