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Single-Shot Accurate 3D Reconstruction Using Structured Light Systems Based on Local Optimization Open Access


Other title
structured light method
Single shot reconstruction
3D reconstruction
Type of item
Degree grantor
University of Alberta
Author or creator
Aslsabbaghpourhokmabadi, Neda
Supervisor and department
Herbert Yang (Computing Science)
Examining committee member and department
Eleni Stroulia (Computing Science)
Pierre Boulanger (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Creating 3D models of objects has been studied extensively for several decades. These models have different applications in different fields. As a result, creating 3D models is an interesting area in computer vision. There are many proposed methods for extracting 3D information of 2D images. One of the most common methods for 3D reconstruction is structured light methods. Although structured light methods can get valuable results of 3D reconstruction, they have limitations. For example, the structured light methods can get dense results on static scenes or get sparse results on dynamic scenes. In static scenes, the structured light method projects several patterns, and it results in dense models. However, in dynamic scenes, the structured light method projects just one pattern since the object is moving, and it results in sparse models. The limitation of the structured light methods in dynamic scenes is the most important motivation for this thesis. In this thesis, a single-shot structured light method is developed to overcome the sparse results in dynamic scenes. In particular, the proposed method can obtain more accurate reconstruction with just one image of dynamic scenes than that of existing methods. The new method applies global and local optimizations to establish dense correspondences. The result of simulated experiments comparison with the ground truth demonstrates that the proposed method in this thesis achieves more accurate results than that of previous methods. Lastly, the technique developed in this thesis is applied to real data in order to obtain high quality 3D reconstruction results. The results of the new method are more accurate compared to previous methods.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Comprehensive 3d animation software.Image rectificationTriangulationC. Albitar, P. Graebling, and C. Doignon. Design of a monochromatic pattern for a robust structured light coding. In Proceedings of the International Conference on Image Processing, pages 529–532, 2007.D. G. Aliaga. and Y. Xu. A self-calibrating method for photogeometric acquisition of 3d objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4):747–754, 2010.J. Batlle, E. Mouaddib, and J. Salvi. Recent progress in coded structured light as a technique to solve the correspondence problem: a survey. 31:963982, July 1989.D. Bergmann. New approach for automatic surface reconstruction with coded light. In Proceedings of Remote Sensing and Reconstruction for ThreeDimensional Objects and Scenes, pages 2–9, San Diego, CA, 1995.J. Y. Bouguet. Camera calibration toolbox for matlab. 2004.K. Boyer and A. Kak. Color-encoded structured light for rapid active ranging. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9:14– 28, January 1989.M. Brown, P. Song, and T.Cham. Image pre-conditioning for out-of-focus projector blur. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:1956 – 1963, 2006.D. Caspi, N. Kiryati, and J. Shamir. Range imaging with adaptive color structured light. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(5):470–480, 1998. 4X. Chen and Y. Yang. Recovering dense stereo depth maps using a single gaussian blurred structured light pattern. International Conference on Computer and Robot Vision (CRV), pages 295 – 302, 2013.T. Clarke and J.G.Fryer. The development of camera calibration methods and models. The Photogrammetric Record, 16:5166, April 1998.I. J. Cox, S. L. Hingorani, S. B. Rao, and B. M. Maggs. A maximum likelihood stereo algorithm. Computer Vision and Image Understanding, 63:542–567, 1996.J. Davis, D. Nehab, R. Ramamoorthi, and S. Rusinkiewicz. Spacetime stereo: A unifying framework for depth from triangulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):296–302, Feb. 2005.D. Desjardins and P. Payeur. Dense stereo range sensing with marching pseudorandom patterns. In Proceedings of the Fourth Canadian Conference on Computer and Robot Vision, pages 216–226, 2007.T. Etzion. Constructions for perfect maps and pseudorandom arrays. IEEE Transactions on Information Theory, 34:1308–1316, September 1988.H. Fredricksen. The lexicographically least debruijn cycle. Journal of Combinatorial Theory, 9(1):509–510, 1970.D. C. Ghiglia and M. D. Pritt. Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. John Wiley and Sons, Inc, 1998. 2L. Goddyn and P. Gvozdjak. Binary gray codes with long bit runs. ELECTRONIC JOURNAL OF COMBINATORICS, 10, 2003.P. M. Griffin, L. S. Narasimhan, and S. R. Yee. Generation of uniquely encoded light patterns for range data acquisition. Pattern Recognition, 25(6):609–616, 1992. xJ. Guhring. Dense 3-d surface acquisition by structured light using off-the-shelf components. In Proceedings of Videometrics and Optical Methods for 3D Shape Measuring, pages 220–231, 2001.X. Han. 3D Shape Measurement Based on the Phase Shifting and Stereovision methods. The Graduate School, Stony Brook University: Stony Brook, NY, May 2010.D. Hirshberg, M. Loper, E. Rachlin, and M. Black. Coregistration: Simultaneous alignment and modeling of articulated 3D shape. In European Conference on Computer Vision, 2012.D. Hirshbergc, M. Loperc, E. Rachlinc, A. Tsoliac, A. Weissa, , B. Cornerb, and M. Blacka. Evaluating the automated alignment of 3d human body scans. 2011.
and.html. How to make 3D scan with pictures.
reconstruction. Passive methods.
. Camera-projector calibration toolbox.
. sensefly.
. Middlebury stereo vision page.
home.html. Photonic technologies and solutions for technical professionals worldwide.P. S. Huang and S. Zhang. Fast three-step phase-shifting algorithm. Applied Optics, 45(21):5086–5091, 2005.P. S. Huang, S. Zhang, and F.-P. Chiang. Trapezoidal phase-shifting method for 3-d shape measurement. Optical Engineering, 44(12), 2005.H. Kawasaki, R. Furukawa, R. Sagawa, and Y. Yagi. Dynamic scene shape reconstruction using a single structured light pattern. In IEEE conference on Computer Vision and Pattern Recognition, pages 1–8, June 2008.M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, and D. Fulk. The Digital Michelangelo Project: 3D scanning of large statues. In Proceedings of ACM SIGGRAPH 2000, pages 131–144, July 2000.M. Minou, T. Kanade, and T. Sakai. A method of time-coded parallel planes of light for depth measurement. Trans. Institute of Electronics and Communication Engineers of Japan, E64(8):521–528, August 1981.R. A. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov. Structured light using pseudorandom codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):322–327, Mar. 1998.J. Pages, J. Salvi, C. Collewet, and J. Forest. Optimised de bruijn patterns for one-shot shape acquisition. Image and Vision Computing, 23(8):707–720, Aug. 2005.J. L. Posdamer and M. D. Altschuler. Surface measurement by space-encoded projected beam system. Computer Graphics and Image Processing, 18(1):1–17, 1982.E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. Color transfer between images. Computer Graphics and Applications, 21:34–41, September 2001.M. Rodrigues. Fast 3D reconstruction using structured light methods. In International Conference on Medical Image Computing and Computer Assisted Intervention, 2011.S. Rusinkiewicz, O. Hall-Holt, and M. Levoy. Real-time 3d model acquisition. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 438–446, 2002.F. Ruskey. The combinatorial object server.J. Salvi, X. Armangue, and J. Batlle. A comparative review of camera calibrating methods with accuracy evaluation. 35:16171635, July 2002.J. Salvi, J. Batlle, and E. Mouaddib. A robust-coded pattern projection for dynamic 3D scene measurement. 19:10551065, September 1989.J. Salvi, J. Pages, and J. Batlle. Pattern codification strategies in structured light systems. Pattern Recognition, 37:827–849, 2004.H. Spoelder, F. Vos, E. Petriu, and F. Groen. Some aspects of pseudo random binary array-based surface characterization. IEEE Transaction on Instrumentation and Measurement, 49:1331–1336, December 2000.R. Valkenburg and A. McIvor. Accurate 3d measurement using a structured light system. Image and Vision Computing, 16:99–110, 1996.X. Wen, H. Wang, and W. Zhai. Medical image based 3d reconstruction and preoperative surgery-planning for microwave ablation. IEEE International Conference onBioinformatics and Biomedicine, pages 38–42, December 2013.L. Zhang, B. Curless, and S. M. Seitz. Rapid shape acquisition using color structured light and multi-pass dynamic programming. In Proceedings of the 1st International Symposium on 3D Data Processing, Visualization, and Transmission, pages 24–36, Padova, Italy, 2002.S. Zhang and P. Huang. High-resolution, real-time 3d shape acquisition. In Proceedings of on Computer Vision and Pattern Recognition Workshop, pages 28–, 2004.S. Zhang, D. Welde, and J. Oliver. Superfast phase-shifting method for 3D shape measurement. In Opt. Express, volume 18. OSA, Apr 2010.S. Zhang and S. T. Yau. High-resolution, real-time absolute 3-d coordinate measurement based on the phase shifting method. Optics Express, 14(7):2664– 2649, 2007.S. Zhang and S. T. Yau. High-speed three-dimensional shape measurement system using a modified two-plus-one phase-shifting algorithm. Optical Engineering, 46(11), 2007.Z. Zhang. A flexible new technique for camera calibration. 22:1330–1334, November 2000.

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