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Structured Light

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Active Lighting and Its Application for Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

The structured light method is a very popular way to find correspondences between coordinates of a camera image and those of a projector image. This method class uses spatial or temporal image patterns as clues to obtain correspondence information. The basic approaches are described in Sect. 2.5, and various extensions have been proposed to apply structured light methods to various situations. In this chapter, the methods are classified based on projection patterns, and several extension methods are introduced.

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References

  1. Keselman L, Iselin Woodfill J, Grunnet-Jepsen A, Bhowmik A (2017) Intel realsense stereoscopic depth cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) workshops, pp 1–10

    Google Scholar 

  2. Kanazawa Y, Kanatani K (1995) Reliability of 3-D reconstruction by stereo vision. IEICE Trans Inf Syst 78(10):1301–1306

    Google Scholar 

  3. Hartley RI, Sturm P (1997) Triangulation. Comput Vis Image Underst J (CVIU) 68(2):146–157

    Article  Google Scholar 

  4. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press

    Google Scholar 

  5. Kanatani K, Sugaya Y, Niitsuma H (2008) Triangulation from two views revisited: hartley-sturm versus optimal correction. Practice 4:5

    Google Scholar 

  6. Lindstrom P (2010) Triangulation made easy. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1554–1561

    Google Scholar 

  7. Yang K, Fang W, Zhao Y, Deng N (2019) Iteratively reweighted midpoint method for fast multiple view triangulation. IEEE Robot Autom Lett 4(2):708–715

    Article  Google Scholar 

  8. Chu CW, Hwang S, Jung SK (2001) Calibration-free approach to 3D reconstruction using light stripe projections on a cube frame. In: Proceedings third international conference on 3-D digital imaging and modeling. IEEE, pp 13–19

    Google Scholar 

  9. Takatsuka M, West GA, Venkatesh S, Caelli TM (1999) Low-cost interactive active monocular range finder. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 1. IEEE, pp 444–449

    Google Scholar 

  10. Furukawa R, Kawasaki H (2003) Interactive shape acquisition using marker attached laser projector. In: Proceedings of the fourth international conference on 3-D digital imaging and modeling, 2003. 3DIM 2003. IEEE, pp 491–498

    Google Scholar 

  11. Furukawa R, Kawasaki H (2009) Laser range scanner based on self-calibration techniques using coplanarities and metric constraints. Comput Vis Image Underst J (CVIU) 113(11):1118–1129

    Article  Google Scholar 

  12. Hall-Holt O, Rusinkiewicz S (2001) Stripe boundary codes for real-time structured-light range scanning of moving objects. In: Proceedings of the international conference on computer vision (ICCV), vol 2. IEEE, pp 359–366

    Google Scholar 

  13. Rusinkiewicz S, Hall-Holt O, Levoy M (2002) Real-time 3D model acquisition. ACM Trans Graph (TOG) 21(3):438–446

    Article  Google Scholar 

  14. Weise T, Leibe B, Van Gool L (2007) Fast 3D scanning with automatic motion compensation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

    Google Scholar 

  15. Zhang L, Snavely N, Curless B, Seitz SM (2004) Spacetime faces: high resolution capture for modeling and animation. In: Proceedings of SIGGRAPH, pp 548–558

    Google Scholar 

  16. DAVIS J (2005) Spacetime stereo: a unifying framework for depth from triangulation. IEEE Trans Pattern Anal Mach Intell (PAMI) 27(2):296–302

    Google Scholar 

  17. Young M, Beeson E, Davis J, Rusinkiewicz S, Ramamoorthi R (2007) Viewpoint-coded structured light. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

    Google Scholar 

  18. Narasimhan SG, Koppal SJ, Yamazaki S (2008) Temporal dithering of illumination for fast active vision. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 830–844

    Google Scholar 

  19. Batlle J, Mouaddib E, Salvi J (1998) Recent progress in coded structured light as a technique to solve the correspondence problem: a survey. Pattern Recogn 31(7):963–982

    Article  Google Scholar 

  20. Koninckx TP, Geys I, Jaeggli T, Van Gool L (2004) A graph cut based adaptive structured light approach for real-time range acquisition. In: Proceedings of the 2nd international symposium on 3D data processing, visualization and transmission, 2004. 3DPVT 2004. IEEE, pp 413–421

    Google Scholar 

  21. Tajima J, Iwakawa M (1990) 3-D data acquisition by rainbow range finder. In: Proceedings of the international conference on pattern recognition (ICPR), vol 1. IEEE, pp 309–313

    Google Scholar 

  22. Zhang L, Curless B, Seitz SM (2002) Rapid shape acquisition using color structured light and multi-pass dynamic programming. In: Proceedings of the first international symposium on 3D data processing visualization and transmission. IEEE, pp 24–36

    Google Scholar 

  23. Maruyama M, Abe S (1993) Range sensing by projecting multiple slits with random cuts. IEEE Trans Pattern Anal Mach Intell (PAMI) 15(6):647–651

    Article  Google Scholar 

  24. Artec (2007) United States Patent Application 2009005924

    Google Scholar 

  25. Koninckx TP, Van Gool L (2006) Real-time range acquisition by adaptive structured light. IEEE Trans Pattern Anal Mach Intell (PAMI) 28(3):432–445

    Article  Google Scholar 

  26. Morano RA, Ozturk C, Conn R, Dubin S, Zietz S, Nissanov J (1998) Structured light using pseudorandom codes. IEEE Trans Pattern Anal Mach Intell (PAMI) 20(3):322–327

    Article  Google Scholar 

  27. Kimura M, Mochimaru M, Kanade T (2008) Measurement of 3D foot shape deformation in motion. In: Proceedings of the 5th ACM/IEEE international workshop on projector camera systems, pp 1–8

    Google Scholar 

  28. Primesense (2010) United States Patent Application US 2010/0118123

    Google Scholar 

  29. Je C, Lee SW, Park RH (2004) High-contrast color-stripe pattern for rapid structured-light range imaging. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 95–107

    Google Scholar 

  30. Pan J, Huang PS, Chiang FP (2005) Color-coded binary fringe projection technique for 3-D shape measurement. Opt Eng 44(2):023606

    Article  Google Scholar 

  31. Salvi J, Batlle J, Mouaddib E (1998) A robust-coded pattern projection for dynamic 3D scene measurement. Pattern Recogn Lett 19(11):1055–1065

    Article  Google Scholar 

  32. Vuylsteke P, Oosterlinck A (1990) Range image acquisition with a single binary-encoded light pattern. IEEE Trans Pattern Anal Mach Intell (PAMI) 12(2):148–164

    Article  Google Scholar 

  33. Kawasaki H, Furukawa R, Sagawa R, Yagi Y (2008) Dynamic scene shape reconstruction using a single structured light pattern. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

    Google Scholar 

  34. Sagawa R, Ota Y, Yagi Y, Furukawa R, Asada N, Kawasaki H (2009) Dense 3D reconstruction method using a single pattern for fast moving object. In: Proceedings of the international conference on computer vision (ICCV). IEEE, pp 1779–1786

    Google Scholar 

  35. Ulusoy AO, Calakli F, Taubin G (2009) One-shot scanning using de Bruijn spaced grids. In: Proceedings of the international conference on computer vision (ICCV) workshops. IEEE, pp 1786–1792

    Google Scholar 

  36. Furukawa R, Kawasaki H (2006) Self-calibration of multiple laser planes for 3D scene reconstruction. In: Third international symposium on 3D data processing, visualization, and transmission (3DPVT’06). IEEE, pp 200–207

    Google Scholar 

  37. Sagawa R, Sakashita K, Kasuya N, Kawasaki H, Furukawa R, Yagi Y (2012) Grid-based active stereo with single-colored wave pattern for dense one-shot 3D scan. In: 2012 second international conference on 3D imaging, modeling, processing, visualization & transmission. IEEE, pp 363–370

    Google Scholar 

  38. Gupta M, Nayar SK (2012) Micro phase shifting. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 813–820

    Google Scholar 

  39. Kim D, Ryu M, Lee S (2008) Antipodal gray codes for structured light. In: Proceedings of the IEEE international conference on robotics and automation (ICRA). IEEE, pp 3016–3021

    Google Scholar 

  40. Gupta M, Agrawal A, Veeraraghavan A, Narasimhan SG (2011) Structured light 3d scanning in the presence of global illumination. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 713–720

    Google Scholar 

  41. Gupta M, Agrawal A, Veeraraghavan A, Narasimhan SG (2013) A practical approach to 3D scanning in the presence of interreflections, subsurface scattering and defocus. Int J Comput Vis (IJCV) 102(1–3):33–55

    Article  Google Scholar 

  42. Couture V, Martin N, Roy S (2014) Unstructured light scanning robust to indirect illumination and depth discontinuities. Int J Comput Vis (IJCV) 108(3):204–221

    Article  MathSciNet  Google Scholar 

  43. Subpa-asa A, Fu Y, Zheng Y, Amano T, Sato I (2018) Separating the direct and global components of a single image. J Inf Process 26:755–767. https://doi.org/10.2197/ipsjjip.26.755

    Article  Google Scholar 

  44. Sagawa R, Kawasaki H, Kiyota S, Furukawa R (2011) Dense one-shot 3D reconstruction by detecting continuous regions with parallel line projection. In: Proceedings of the international conference on computer vision (ICCV). IEEE, pp 1911–1918

    Google Scholar 

  45. Furukawa R, Sagawa R, Kawasaki H, Sakashita K, Yagi Y, Asada N (2010) One-shot entire shape acquisition method using multiple projectors and cameras. In: Pacific-Rim symposium on image and video technology (PSVIT). IEEE, pp 107–114

    Google Scholar 

  46. Kasuya N, Sagawa R, Kawasaki H, Furukawa R (2013) Robust and accurate one-shot 3D reconstruction by 2c1p system with wave grid pattern. In: 2013 International Conference on 3D Vision-3DV 2013. IEEE, pp 247–254

    Google Scholar 

  47. Kasuya N, Sagawa R, Furukawa R, Kawasaki H (2013) One-shot entire shape scanning by utilizing multiple projector-camera constraints of grid patterns. In: Proceedings of the international conference on computer vision (ICCV) workshops, pp 299–306

    Google Scholar 

  48. Sagawa R, Kasuya N, Oki Y, Kawasaki H, Matsumoto Y, Furukawa R (2014) 4D capture using visibility information of multiple projector camera system. In: 2014 2nd international conference on 3D vision, vol 2. IEEE, pp 14–21

    Google Scholar 

  49. Sagawa R, Kawamura T, Furukawa R, Kawasaki H, Matsumoto Y (2014) One-shot 3D reconstruction of moving objects by projecting wave grid pattern with diffractive optical element. In: Proceedings of the 11th IMEKO symposium laser metrology for precision measurement and inspection in industry (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Applied Robotics Research, Microsoft Corporation, Redmond, WA, USA

    Katsushi Ikeuchi

  2. Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

    Yasuyuki Matsushita

  3. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan

    Ryusuke Sagawa

  4. Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan

    Hiroshi Kawasaki

  5. Division of Information Science, Nara Institute of Science and Technology, Nara, Japan

    Yasuhiro Mukaigawa

  6. Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan

    Ryo Furukawa

  7. Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan

    Daisuke Miyazaki

Authors
  1. Katsushi Ikeuchi
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  2. Yasuyuki Matsushita
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  3. Ryusuke Sagawa
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  4. Hiroshi Kawasaki
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  5. Yasuhiro Mukaigawa
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  6. Ryo Furukawa
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  7. Daisuke Miyazaki
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Correspondence to Katsushi Ikeuchi .

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Ikeuchi, K. et al. (2020). Structured Light. In: Active Lighting and Its Application for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-56577-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-56577-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56576-3

  • Online ISBN: 978-3-030-56577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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