This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Spacetime Stereo: A Unifying Framework for Depth from Triangulation
February 2005 (vol. 27 no. 2)
pp. 296-302
Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. In this paper, we propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.

[1] K. Araki, Y. Sato, and S. Parthasarathy, “High Speed Rangefinder,” Proc. SPIE Optics, Illumination, and Image Sensing for Machine Vision, vol. 850, pp. II-184-II-188, 1987.
[2] 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.
[3] P. Besl, Active Optical Range Imaging Sensors, in Advances in Machine Vision, chapter 1, pp. 1-63, 1989.
[4] M.J. Black and P. Anandan, “A Framework for the Robust Estimation of Optical Flow,” Proc. Fourth Int'l Conf. Computer Vision, pp. 231-236, 1993.
[5] R. Bolles, H. Baker, and D. Marimont, “Epipolar-Plane Image Analysis: An Approach to Determining Structure from Motion,” Int'l J. Computer Vision, pp. 7-56, 1987.
[6] N. Borghese, G. Ferrigno, G. Baroni, A. Pedotti, S. Ferrari, and R. Savare, “Autoscan: A Flexible and Portable 3D Scanner,” IEEE Computer Graphics and Applications, vol. 18, no. 3, pp. 38-41, 1998.
[7] J. Bouguet and P. Perona, “3D Photography on Your Desk,” Proc. Fourth Int'l Conf. Computer Vision, pp. 43-50 1998.
[8] K.L. Boyer and A.C. Kak, “Color-Encoded Structured Light for Rapid Active Ranging,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 1, 1987.
[9] Y. Boykov, O. Veksler, and R. Zabih, “Fast Approximate Energy Minimization via Graph Cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, Nov. 2001.
[10] C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range Data Acquisition Using Color Structured Lighting and Stereo Vision,” Image and Vision Computing, vol. 15, no. 6, pp. 445-456, June 1997.
[11] B. Curless, “Overview of Active Vision Techniques,” Proc. SIGGRAPH 99 Course on 3D Photography, 1999.
[12] B. Curless and M. Levoy, “Better Optical Triangulation through Spacetime Analysis,” Proc. Int'l Conf. Computer Vision, pp. 987-994, 1995.
[13] J. Davis and X. Chen, “A Laser Range Scanner Designed for Minimum Calibration Complexity,” Proc. Third Int'l Conf. 3D Digital Imaging and Modeling, 2001.
[14] J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime Stereo: A Unifying Framework for Depth from Triangulation,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. II-359-II-366, 2003.
[15] U. Dhond and J. Aggarwal, “Structure from Stereo— A Review,” IEEE Trans. Systems, Man, and Cybernetics, vol. 19, no. 6, 1989.
[16] O. Hall-Holt and S. Rusinkiewicz, “Stripe Boundary Codes for Real-Time Structured-Light Range Scanning of Moving Objects,” Proc. Int'l Conf. Computer Vision, pp. 359-366, 2001.
[17] S. Inokuchi, K. Sato, and F. Matsuda, “Range-Imaging for 3D Object Recognition,” Proc. Int'l Conf. Pattern Recognition, pp. 806-808, 1984.
[18] R. Jarvis, “A Perspective on Range Finding Techniques for Computer Vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, no. 2, pp. 122-139, 1983.
[19] T. Kanade, A. Gruss, and L. Carley, “A Very Fast VLSI Rangefinder,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 1322-1329, 1991.
[20] S. Kang, J. Webb, C. Zitnick, and T. Kanade, “A Multibaseline Stereo System with Active Illumination and Real-Time Image Acquisition,” Proc. Int'l Conf. Computer Vision, pp. 88-93, 1995.
[21] G. Medioni and J. Jezouin, “An Implementation of an Active Stereo Range Finder,” Optical Soc. Am. Technical Digest Series, vol. 12, pp. 34-51, 1987.
[22] Y. Ohta and T. Kanade, “Stereo by Intra- and Inter-Scaline Search Using Dynamic Programming,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, no. 2, 1985.
[23] M. Okutomi and T. Kanade, “A Locally Adaptive Window for Signal Matching,” Int'l J. Computer Vision, vol. 7, no. 2, 1992.
[24] D. Poussart and D. Laurendeau, 3-D Sensing for Industrial Computer Vision, in Advances in Machine Vision, chapter 3, pp. 122-159, 1989.
[25] K. Pulli, H. Abi-Rached, T. Duchamp, L. Shapiro, and W. Stuetzle, “Acquisition and Visualization of Colored 3D Objects,” Proc. Int'l Conf. Pattern Recognition, pp. 11-15, 1998.
[26] S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-Time 3D Model Acquisition,” ACM Trans. Graphics, vol. 21, no. 3, pp. 438-446, 2002.
[27] P. Saint-Marc, J. Jezouin, and G. Medioni, “A Versatile PC-Based Range Finding System,” IEEE Trans. Robotics and Automation, vol. 7, no. 2, pp. 250-256, 1991.
[28] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” Int'l J. Computer Vision, vol. 47, no. 1, pp. 7-42, 2002.
[29] D. Scharstein and R. Szeliski, “High-Accuracy Stereo Depth Maps Using Structured Light,” Proc. Computer Vision and Pattern Recognition, 2003.
[30] E. Shechtman, Y. Caspi, and M. Irani, “Increasing Space-Time Resolution in Video,” Proc. European Conf. Computer Vision, 2002.
[31] T.C. Strand, “Optical Three-Dimensional Sensing for Machine Vision,” Optical Eng., vol. 24, no. 1, pp. 33-40, 1985.
[32] L. Zhang, B. Curless, and S. Seitz, “Rapid Shape Acquisition Using Color Structured Light and Multi-Pass Dynamic Programming,” IEEE 3D Data Processing Visualization and Transmission, 2002.
[33] L. Zhang, B. Curless, and S. Seitz, “Spacetime Stereo: Shape Recovery for Dynamic Scenes,” Proc. Computer Vision and Pattern Recognition, 2003.

Index Terms:
Depth from triangulation, stereo, spacetime stereo.
Citation:
James Davis, Diego Nehab, Ravi Ramamoorthi, Szymon Rusinkiewicz, "Spacetime Stereo: A Unifying Framework for Depth from Triangulation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 296-302, Feb. 2005, doi:10.1109/TPAMI.2005.37
Usage of this product signifies your acceptance of the Terms of Use.