Abstract
Consumer depth cameras belong to two technological families; structured light depth cameras employ active triangulation, while matricial Time-of-Flight cameras operate on the basis of the Time-of-Flight (ToF) principle. We introduce foundational concepts for understanding both families and cover the computer vision concepts behind pin-hole imaging, camera calibration, 2-view, and N-view stereo which lie at the heart of operating structured light cameras. The chapter also introduces the Time-of-Flight (ToF) principle and modulation types used in point-wise ToF measurements, as well as operations of the matricial ToF sensors which ToF depth cameras rely on.
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Notes
- 1.
It is worth to notice that the phase \(\varphi _{c}\) of the carrier at the transmitter side is generally different from the phase \(\varphi _{c}^{{\prime}}\) at the receiver. Both \(\varphi _{c}\) and \(\varphi _{c}^{{\prime}}\) are usually unknown, especially in the case of a non-coherent process which is the typical practical solution. However, the system does not need to be aware of the values of \(\varphi _{c}\) and \(\varphi _{c}^{{\prime}}\) and it is inherently robust to the lack of their knowledge.
References
Faro, http://faro.com Accessed March 2016
Iee, http://www.iee.lu Accessed March 2016
Intel RealSense, www.intel.com/realsense Accessed March 2016
Leica, http://hds.leica-geosystems.com Accessed March 2016
Mesa imaging, http://www.mesa-imaging.ch Accessed March 2016
OpenCV, http://opencv.org Accessed March 2016
Pmd technologies, http://www.pmdtec.com/ Accessed March 2016
Riegl, http://www.riegl.com/ Accessed March 2016
Velodyne lidar, http://www.velodynelidar.com Accessed March 2016
Zoller and Frolich, http://www.zf-laser.com/ Accessed March 2016
J. Andrews, N. Baker, Xbox 360 system architecture. IEEE Micro 26(2), 25–37 (2006)
C.S. Bamji, P. O’Connor, T. Elkhatib, S. Mehta, B. Thompson, L.A. Prather, D. Snow, O.C. Akkaya, A. Daniel, A.D. Payne, T. Perry, M. Fenton, V.-H. Chan, A 0.13 um cmos system-on-chip for a 512 × 424 time-of-flight image sensor with multi-frequency photo-demodulation up to 130 mhz and 2 gs/s adc. IEEE J. Solid-State Circuits 50(1), 303–319 (2015)
Y. Bar-Shalom, Tracking and Data Association (Academic Press Professional, Inc., San Diego, CA, 1987)
F. Bernardini, H.E. Rushmeier, The 3d model acquisition pipeline. Comput. Graphics Forum 21(2), 149–172 (2002)
G. Borenstein, Making Things See: 3D Vision with Kinect, Processing, Arduino, and MakerBot (Maker Media, O’Reilly Media Inc., Sebastopol, 2012)
J.Y. Bouguet, Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed March 2016
J.Y. Bouguet, B. Curless, P. Debevec, M. Levoy, S. Nayar, S. Seitz, Overview of active vision techniques, in Proceedings of ACM SIGGRAPH Workshop, Course on 3D Photography (2000)
D. Claus, A.W. Fitzgibbon, A rational function lens distortion model for general cameras, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)
B. Curless, M. Levoy, A volumetric method for building complex models from range images, in Proceedings of ACM SIGGRAPH (New York, 1996), pp. 303–312
B. Cyganek, An Introduction to 3D Computer Vision Techniques and Algorithms (Wiley, New York, 2007)
D. Dardari, A. Conti, U. Ferner, A. Giorgetti, M.Z. Win, Ranging with ultrawide bandwidth signals in multipath environments. Proc. IEEE 97(2), 404–426 (2009)
E.R. Davies, Computer and Machine Vision, 4th edn. (Academic, Boston, 2012)
J. Davis, D. Nehab, R. Ramamoorthi, S. Rusinkiewicz, Spacetime stereo: a unifying framework for depth from triangulation, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2003)
O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint (MIT Press, Cambridge, 1993)
M.A. Fischler, R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, in Readings in Computer Vision: Issues, Problems, Principles and Paradigms, vol. 1 (M. Kaufmann Publishers, Los Altos, CA, 1987), pp. 726–740
D.A. Forsyth, J. Ponce, Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference (Prentice Hall, London, 2002)
A. Fusiello, Visione Computazionale. Tecniche di Ricostruzione Tridimensionale (Franco Angeli, Milano, 2013)
A. Fusiello, E. Trucco, A. Verri, A compact algorithm for rectification of stereo pairs. Mach. Vis. Appl. 12, 16–22 (2000)
C. Harris, M. Stephens, A combined corner and edge detector. in Proceedings of Alvey Vision Conference (1988), pp. 147–151
R.I. Hartley, In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)
R.I. Hartley, P. Sturm, Triangulation, in Procedings of ARPA Image Understanding Workshop (1994)
R.I. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University Press, Cambridge, 2004)
J. Heikkila, O. Silven, A four-step camera calibration procedure with implicit image correction, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1997)
A. Heyden, K. Astrom, Euclidean reconstruction from constant intrinsic parameters. in Proceedings of International Conference on Pattern Recognition, pp. 339–343
T.S. Huang, O. Faugeras, Some properties of the E matrix in two-view motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 11(12), 1310–1312 (1989)
K. Konolige, Projected texture stereo, in Proceedings of IEEE International Conference on Robotics and Automation (2010)
K. Konolige, Sparse sparse bundle adjustment, in Proceedings of British Machine Vision Conference (BMVA Press, Aberystwyth, 2010), pp. 102.1–102.11
R. Lange, 3D Time-of-flight distance measurement with custom solid-state image sensors in CMOS/CCD-technology, Ph.D. thesis, University of Siegen (2000)
M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, D. Fulk, The digital michelangelo project: 3d scanning of large statues, in Proceedings of ACM SIGGRAPH (Addison-Wesley Publishing Co., New York, 2000), pp. 131–144
H. Li, R. Hartley, Five-point motion estimation made easy, in Proceedings of International Conference on Pattern Recognition (2006), pp. 630–633
C. Loop, Z. Zhang, Computing rectifying homographies for stereo vision, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (1999), p. 131
D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in Proceedings of International Joint Conference on Artificial Intelligence, (Morgan Kaufmann Publishers Inc., San Francisco, CA, 1981), pp. 674–679
Q.T. Luong, O.D. Faugeras, The fundamental matrix: theory, algorithms, and stability analysis. Int. J. Comput. Vis. 17, 43–75 (1995)
Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry, An Invitation to 3-D Vision: From Images to Geometric Models (Springer, Berlin, 2003)
P.R.S. Mendonca, R. Cipolla, A simple technique for self-calibration in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1999), p. 505
K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
S.J.D. Prince, Computer Vision: Models, Learning, and Inference, 1st edn. (Cambridge University Press, New York, 2012)
L. Robert, O. Faugeras, Relative 3d positioning and 3d convex hull computation from a weakly calibrated stereo pair, in Proceedings of International Conference on Computer Vision (1993), pp. 540–544
J. Salvi, J. Pagès, J. Batlle, Pattern codification strategies in structured light systems. Pattern Recogn. 37, 827–849 (2004)
D. Scharstein, R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3), 7–42 (2001)
R. Schwarte et al., Pseudo-noise (pn) laser radar without scanner for extremely fast 3d-imaging and navigation, in Proceedings of Microwave and Optronics Conference (1997)
N. Snavely, Bundler, http://www.cs.cornell.edu/~snavely/bundler/. Accessed March 2016
N. Snavely, S.M. Seitz, R. Szeliski, Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)
G. Stockman, L.G. Shapiro, Computer Vision, 1st edn. (Prentice Hall PTR, Upper Saddle River, 2001)
D. Stoppa, F. Remondino (eds.), TOF Range-Imaging Cameras (Springer, Berlin, 2012)
R. Szeliski, Computer Vision: Algorithms and Applications (Springer, New York, 2010)
C. Tomasi, T. Kanade, Detection and tracking of point features. Technical report, International Journal of Computer Vision (1991)
B. Triggs, P.F. McLauchlan, R.I. Hartley, A.W. Fitzgibbon, Bundle adjustment - a modern synthesis, in Proceedings of ICCV Workshop, Vision Algorithms: Theory and Practice (Springer, London, 2000), pp. 298–372
M. Trobina, Error model of a coded-light range sensor. Technical report, Communication Technology Laboratory Image Science Group, ETH-Zentrum (1995)
E. Trucco, A. Verri, Introductory Techniques for 3-D Computer Vision (Prentice Hall PTR, Upper Saddle River, 1998)
Z. Xu, Investigation of 3D-Imaging Systems Based on Modulated Light and Optical RF-Interferometry (Shaker Verlag GmbH, Aachen, 1999)
Z. Zhang, T. Kanade, Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vis. 27, 161–195 (1998)
L. Zhang, B. Curless, S.M. Seitz, Rapid shape acquisition using color structured light and multi-pass dynamic programming, in Proceedings of IEEE International Symposium on 3D Data Processing, Visualization, and Transmission (2002), pp. 24–36
L. Zhang, B. Curless, S.M. Seitz, Spacetime stereo: shape recovery for dynamic scenes, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2003)
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Zanuttigh, P., Marin, G., Dal Mutto, C., Dominio, F., Minto, L., Cortelazzo, G.M. (2016). Introduction. In: Time-of-Flight and Structured Light Depth Cameras. Springer, Cham. https://doi.org/10.1007/978-3-319-30973-6_1
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DOI: https://doi.org/10.1007/978-3-319-30973-6_1
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