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A Comparative Survey of Geometric Light Source Calibration Methods

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Pattern Recognition (DAGM GCPR 2021)

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Abstract

With this survey paper, we provide a comprehensive overview of geometric light source calibration methods developed in the last two decades and a comparison of those methods with respect to key properties such as dominant lighting cues, time performance and accuracy. In addition, we discuss different light source models and propose a corresponding categorization of the calibration methods. Finally, we discuss the main application areas of light source calibration and seek to inspire a more unified approach with respect to evaluation metrics and data sets used in the research community.

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References

  1. Ackermann, J., Fuhrmann, S., Goesele, M.: Geometric point light source calibration. In: Bronstein, M., Favre, J., Hormann, K. (eds.) Vision, Modeling & Visualization. The Eurographics Association (2013)

    Google Scholar 

  2. Alhakamy, A., Tuceryan, M.: Real-time illumination and visual coherence for photorealistic augmented/mixed reality. ACM Comput. Surv. 53(3), 1–34 (2020)

    Article  Google Scholar 

  3. Alldrin, N., Zickler, T., Kriegman, D.: Photometric stereo with non-parametric and spatially-varying reflectance. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)

    Google Scholar 

  4. Alldrin, N., Kriegman, D.: A planar light probe. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 2324–2330 (2006)

    Google Scholar 

  5. Aoto, T., Taketomi, T., Sato, T., Mukaigawa, Y., Yokoya, N.: Position estimation of near point light sources using a clear hollow sphere. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3721–3724 (2012)

    Google Scholar 

  6. Arief, I., McCallum, S., Hardeberg, J.Y.: Realtime estimation of illumination direction for augmented reality on mobile devices. In: Color and Imaging Conference, vol. 2012, pp. 111–116. Society for Imaging Science and Technology (2012)

    Google Scholar 

  7. Boom, B., Orts-Escolano, S., Ning, X., McDonagh, S., Sandilands, P., Fisher, R.: Point light source estimation based on scenes recorded by a RGB-D camera. In: British Machine Vision Conference, Bristol (2013)

    Google Scholar 

  8. Bunteong, A., Chotikakamthorn, N.: Light source estimation using feature points from specular highlights and cast shadows. Int. J. Phys. Sci. 11, 168–177 (2016)

    Article  Google Scholar 

  9. Burley, B., Studios, W.D.A.: Physically-based shading at disney. In: ACM SIGGRAPH, vol. 2012, pp. 1–7 (2012)

    Google Scholar 

  10. Cao, X., Foroosh, H.: Camera calibration and light source orientation from solar shadows. Comput. Vis. Image Underst. 105(1), 60–72 (2007)

    Article  Google Scholar 

  11. Chabert, C.F., et al.: Relighting human locomotion with flowed reflectance fields. In: ACM SIGGRAPH 2006 Sketches, pp. 76–es (2006)

    Google Scholar 

  12. Chen, G., Han, K., Shi, B., Matsushita, Y., Wong, K.Y.K.K.: Self-calibrating deep photometric stereo networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8731–8739 (2019)

    Google Scholar 

  13. Debevec, P.: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1998, pp. 189–198. Association for Computing Machinery, New York (1998)

    Google Scholar 

  14. Debevec, P.: A median cut algorithm for light probe sampling. In: ACM SIGGRAPH 2005 Posters, SIGGRAPH 2005, pp. 66–es. Association for Computing Machinery, New York (2005)

    Google Scholar 

  15. Dong, Y., Chen, G., Peers, P., Zhang, J., Tong, X.: Appearance-from-motion: recovering spatially varying surface reflectance under unknown lighting. ACM Trans. Graph. (TOG) 33(6), 1–12 (2014)

    Article  Google Scholar 

  16. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  17. Frahm, J.M., Koeser, K., Grest, D., Koch, R.: Markerless augmented reality with light source estimation for direct illumination. In: The 2nd IEE European Conference on Visual Media Production CVMP 2005, pp. 211–220 (2005)

    Google Scholar 

  18. Fujimura, Y., Iiyama, M., Hashimoto, A., Minoh, M.: Photometric stereo in participating media considering shape-dependent forward scatter. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7445–7453 (2018)

    Google Scholar 

  19. Furukawa, Y., Hernández, C.: Multi-view stereo: a tutorial. Found. Trends. Comput. Graph. Vis. 9(1–2), 1–148 (2015)

    Article  Google Scholar 

  20. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014)

    Article  Google Scholar 

  21. Goldman, D.B., Curless, B., Hertzmann, A., Seitz, S.M.: Shape and spatially-varying BRDFs from photometric stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1060–1071 (2010)

    Article  Google Scholar 

  22. Gruber, L., Richter-Trummer, T., Schmalstieg, D.: Real-time photometric registration from arbitrary geometry. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 119–128 (2012)

    Google Scholar 

  23. Hara, K., Nishino, K., Ikeuchi, K.: Light source position and reflectance estimation from a single view without the distant illumination assumption. IEEE Trans. Pattern Anal. Mach. Intell. 27, 493–505 (2005)

    Article  Google Scholar 

  24. Hatzitheodorou, M.: Shape from shadows: a Hilbert space setting. J. Complex. 14(1), 63–84 (1998)

    Article  MathSciNet  Google Scholar 

  25. Horn, B.K.: Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Technical report, Massachusetts Institute of Technology (1970)

    Google Scholar 

  26. Innmann, M., Süßmuth, J., Stamminger, M.: BRDF-reconstruction in photogrammetry studio setups. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 3346–3354 (2020)

    Google Scholar 

  27. Jiddi, S., Robert, P., Marchand, E.: Reflectance and illumination estimation for realistic augmentations of real scenes. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct), pp. 244–249 (2016)

    Google Scholar 

  28. Jiddi, S., Robert, P., Marchand, E.: Estimation of position and intensity of dynamic light sources using cast shadows on textured real surfaces. In: 25th IEEE International Conference on Image Processing (ICIP), pp. 1063–1067 (2018)

    Google Scholar 

  29. Jiddi, S., Robert, P., Marchand, E.: Detecting specular reflections and cast shadows to estimate reflectance and illumination of dynamic indoor scenes. IEEE Trans. Vis. Comput. Graph. 1 (2020, online). https://doi.org/10.1109/tvcg.2020.2976986

  30. Kán, P., Kafumann, H.: DeepLight: light source estimation for augmented reality using deep learning. Vis. Comput. 35(6), 873–883 (2019)

    Article  Google Scholar 

  31. Karaoglu, S., Liu, Y., Gevers, T., Smeulders, A.W.M.: Point light source position estimation from RGB-D images by learning surface attributes. IEEE Trans. Image Process. 26(11), 5149–5159 (2017)

    Article  MathSciNet  Google Scholar 

  32. Kasper, M., Heckman, C.: Multiple point light estimation from low-quality 3D reconstructions. In: 2019 International Conference on 3D Vision (3DV), pp. 738–746 (2019)

    Google Scholar 

  33. Knorr, S.B., Kurz, D.: Real-time illumination estimation from faces for coherent rendering. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 113–122. IEEE (2014)

    Google Scholar 

  34. Kronander, J., Banterle, F., Gardner, A., Miandji, E., Unger, J.: Photorealistic rendering of mixed reality scenes. Comput. Graph. Forum 34(2), 643–665 (2015)

    Article  Google Scholar 

  35. Lagger, P., Fua, P.: Using specularities to recover multiple light sources in the presence of texture. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 587–590 (2006)

    Google Scholar 

  36. Langer, M., Zucker, S.: What is a light source? In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 172–178 (1997)

    Google Scholar 

  37. Lee, S., Jung, S.K.: Estimation of illuminants for plausible lighting in augmented reality. In: International Symposium on Ubiquitous Virtual Reality, pp. 17–20 (2011)

    Google Scholar 

  38. Lensch, H.P.A., Kautz, J., Goesele, M., Heidrich, W., Seidel, H.P.: Image-based reconstruction of spatial appearance and geometric detail. ACM Trans. Graph. 22(2), 234–257 (2003)

    Article  Google Scholar 

  39. Li, Y., Lin, Lu, H., Shum, H.Y.: Multiple-cue illumination estimation in textured scenes. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1366–1373 (2003)

    Google Scholar 

  40. Liu, C., Narasimhan, S., Dubrawski, A.: Near-light photometric stereo using circularly placed point light sources. In: IEEE International Conference on Computational Photography (ICCP), pp. 1–10 (2018)

    Google Scholar 

  41. Liu, Y., Kwak, Y.S., Jung, S.K.: Position estimation of multiple light sources for augmented reality. In: Park, J., Stojmenovic, I., Jeong, H., Yi, G. (eds.) Computer Science and its Applications. LNEE, vol. 330, pp. 891–897. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-45402-2_126

    Chapter  Google Scholar 

  42. Lombardi, S., Nishino, K.: Reflectance and natural illumination from a single image. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 582–595. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_42

    Chapter  Google Scholar 

  43. Lopez-Moreno, J., Garces, E., Hadap, S., Reinhard, E., Gutiérrez, D.: Multiple light source estimation in a single image. In: Computer Graphics Forum, vol. 32 (2013)

    Google Scholar 

  44. Luo, T., Wang, G.: Compact collimators designed with point approximation for light-emitting diodes. Light. Res. Technol. 50(2), 303–315 (2018)

    Article  Google Scholar 

  45. Ma, L., Liu, J., Pei, X., Hu, Y., Sun, F.: Calibration of position and orientation for point light source synchronously with single image in photometric stereo. Opt. Express 27(4), 4024–4033 (2019)

    Article  Google Scholar 

  46. Mandl, D., et al.: Learning lightprobes for mixed reality illumination. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 82–89 (2017)

    Google Scholar 

  47. Marques., B.A.D., Drumond., R.R., Vasconcelos., C.N., Clua., E.: Deep light source estimation for mixed reality. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP, pp. 303–311. SciTePress (2018)

    Google Scholar 

  48. Masselus, V., Dutré, P., Anrys, F.: The free-form light stage. In: Debevec, P., Gibson, S. (eds.) Eurographics Workshop on Rendering. The Eurographics Association (2002)

    Google Scholar 

  49. Meister, G., Wiemker, R., Monno, R., Spitzer, H., Strahler, A.: Investigation on the torrance-sparrow specular BRDF model. In: IGARSS 1998. Sensing and Managing the Environment. IEEE International Geoscience and Remote Sensing. Symposium Proceedings (Cat. No.98CH36174), vol. 4, pp. 2095–2097 (1998)

    Google Scholar 

  50. Mo, Z., Shi, B., Lu, F., Yeung, S.K., Matsushita, Y.: Uncalibrated photometric stereo under natural illumination. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2936–2945 (2018)

    Google Scholar 

  51. Moreno, I., Avendaño-Alejo, M., Tsonchev, R.: Designing light-emitting diode arrays for uniform near-field irradiance. Appl. Opt. 45, 2265–2272 (2006)

    Article  Google Scholar 

  52. Mori, K., Watanabe, E., Watanabe, K., Katagiri, S.: Estimation of object color, light source color, and direction by using a cuboid. Syst. Comput. Jpn. 36, 1–10 (2005)

    Google Scholar 

  53. Murez, Z., Treibitz, T., Ramamoorthi, R., Kriegman, D.: Photometric stereo in a scattering medium. In: IEEE International Conference on Computer Vision (ICCV), pp. 3415–3423 (2015)

    Google Scholar 

  54. Nie, Y., Song, Z., Ji, M., Zhu, L.: A novel calibration method for the photometric stereo system with non-isotropic led lamps. In: IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 289–294 (2016)

    Google Scholar 

  55. Nieto, G., Jiddi, S., Robert, P.: Robust point light source estimation using differentiable rendering. CoRR abs/1812.04857 (2018). http://arxiv.org/abs/1812.04857

  56. Nishino, K., Nayar, S.K.: Eyes for relighting. ACM Trans. Graph. (TOG) 23(3), 704–711 (2004)

    Article  Google Scholar 

  57. Ohno, Y.: NIST measurement services: photometric calibrations, vol. 250–37. Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg (1997)

    Google Scholar 

  58. Papadhimitri, T., Favaro, P.: Uncalibrated near-light photometric stereo. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)

    Google Scholar 

  59. Park, J., Sinha, S.N., Matsushita, Y., Tai, Y.W., Kweon, I.S.: Calibrating a non-isotropic near point light source using a plane. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2267–2274 (2014)

    Google Scholar 

  60. Pentland, A.P.: Finding the illuminant direction. J. Opt. Soc. Am. 72(4), 448–455 (1982)

    Article  Google Scholar 

  61. Powell, M.W., Sarkar, S., Goldgof, D.: A simple strategy for calibrating the geometry of light sources. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 1022–1027 (2001)

    Article  Google Scholar 

  62. Ramamoorthi, R., Hanrahan, P.: A signal-processing framework for inverse rendering. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 117–128. Association for Computing Machinery, New York (2001)

    Google Scholar 

  63. Richter-Trummer, T., Kalkofen, D., Park, J., Schmalstieg, D.: Instant mixed reality lighting from casual scanning. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 27–36 (2016)

    Google Scholar 

  64. Santo, H., Waechter, M., Lin, w.y., Sugano, Y., Matsushita, Y.: Light structure from pin motion: Geometric point light source calibration. Int. J. Comput. Vis. 128, 1889–1912 (2020)

    Google Scholar 

  65. Sato, I., Sato, Y., Ikeuchi, K.: Illumination from shadows. IEEE Trans. Pattern Anal. Mach. Intell. 25(3), 290–300 (2003)

    Article  Google Scholar 

  66. Shafer, S.A.: Using color to separate reflection components. Color Res. Appl. 10(4), 210–218 (1985)

    Article  Google Scholar 

  67. Shen, H.L., Cheng, Y.: Calibrating light sources by using a planar mirror. J. Electron. Imaging 20, 013002 (2011)

    Article  Google Scholar 

  68. Shi, B., Wu, Z., Mo, Z., Duan, D., Yeung, S.K., Tan, P.: A benchmark dataset and evaluation for non-lambertian and uncalibrated photometric stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3707–3716 (2016)

    Google Scholar 

  69. Takai, T., Maki, A., Matsuyama, T.: Self shadows and cast shadows in estimating illumination distribution. In: 4th European Conference on Visual Media Production, pp. 1–10 (2007)

    Google Scholar 

  70. Unger, J., Kronander, J., Larsson, P., Gustavson, S., Ynnerman, A.: Temporally and spatially varying image based lighting using HDR-video. In: 21st European Signal Processing Conference (EUSIPCO 2013), pp. 1–5 (2013)

    Google Scholar 

  71. Wang, T.Y., Ritschel, T., Mitra, N.: Joint material and illumination estimation from photo sets in the wild. In: International Conference on 3D Vision (3DV), pp. 22–31 (2018)

    Google Scholar 

  72. Wang, Y., Samaras, D.: Estimation of multiple directional light sources for synthesis of mixed reality images. In: 10th Pacific Conference on Computer Graphics and Applications, pp. 38–47. IEEE Computer Society (2002)

    Google Scholar 

  73. Weber, M., Cipolla, R.: A practical method for estimation of point light-sources. In: Proceedings of BMVC 2001, vol. 2, pp. 471–480 (2001)

    Google Scholar 

  74. Wong, K.-Y.K., Schnieders, D., Li, S.: Recovering light directions and camera poses from a single sphere. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 631–642. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_48

    Chapter  Google Scholar 

  75. Woodham, R.J.: Photometric method for determining surface orientation from multiple images, pp. 513–531. MIT Press (1989)

    Google Scholar 

  76. Xie, L., Song, Z., Huang, X.: A novel method for the calibration of an led-based photometric stereo system. In: IEEE International Conference on Information and Automation (ICIA), pp. 780–783 (2013)

    Google Scholar 

  77. Xie, L., Song, Z., Jiao, G., Huang, X., Jia, K.: A practical means for calibrating an led-based photometric stereo system. Opt. Lasers Eng. 64, 42–50 (2015)

    Article  Google Scholar 

  78. Xu, S., Wallace, A.M.: Recovering surface reflectance and multiple light locations and intensities from image data. Pattern Recogn. Lett. 29(11), 1639–1647 (2008)

    Article  Google Scholar 

  79. Zhang, Y., Yang, Y.H.: Multiple illuminant direction detection with application to image synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 915–920 (2001)

    Article  Google Scholar 

  80. Zhou, W., Kambhamettu, C.: Estimation of the size and location of multiple area light sources. In: International Conference on Pattern Recognition, vol. 4, pp. 214–217. IEEE Computer Society (2004)

    Google Scholar 

  81. Zhou, W., Kambhamettu, C.: Estimation of illuminant direction and intensity of multiple light sources. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 206–220. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47979-1_14

    Chapter  Google Scholar 

  82. Zhou, W., Kambhamettu, C.: A unified framework for scene illuminant estimation. Image Vis. Comput. 26(3), 415–429 (2008)

    Article  Google Scholar 

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Acknowledgments

This work was partially supported by the Thüringer Aufbaubank (TAB), the Free State of Thuringia and the European Regional Development Fund (EFRE) under project number 2019 FGI 0026.

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Authors and Affiliations

  1. Bauhaus-Universität Weimar, Weimar, Germany

    Mariya Kaisheva & Volker Rodehorst

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  1. Mariya Kaisheva
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  2. Volker Rodehorst
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Correspondence to Mariya Kaisheva .

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Editors and Affiliations

  1. Fraunhofer IAIS, Sankt Augustin, Germany

    Christian Bauckhage

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Alexander Schwing

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Kaisheva, M., Rodehorst, V. (2021). A Comparative Survey of Geometric Light Source Calibration Methods. In: Bauckhage, C., Gall, J., Schwing, A. (eds) Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science(), vol 13024. Springer, Cham. https://doi.org/10.1007/978-3-030-92659-5_43

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