Skip to main content
SpringerLink
Log in

Photometric Stereo

  • Chapter
  • First Online:
Active Lighting and Its Application for Computer Vision

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

  • 782 Accesses

Abstract

Shading on a surface provides strong cues for us to perceive the 3D shape of an object. The study of computationally inferring a 3D shape from shading cues originated with shape from shading (Horn in Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Technical report, 1970, [1], Ikeuchi and Horn in Artif Intell 17(1–3):141–184, 1981, [2]), which aims to estimate a 3D shape from a single image observed under a light source. The problem has attracted many researchers’ attentions since then because of its mathematically rich problem structure and important practical applications. Later, it was shown that, instead of a single image, the use of multiple images observed under different lighting conditions could alleviate the difficulty of the problem of 3D shape estimation from shading cues. This multiple-image approach is known as photometric stereo. This chapter describes the problem of photometric stereo and its solution methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    \(\mathcal {S}^2 = \{\mathbf {v} \in \mathbb {R}^3 : \Vert \mathbf {v}\Vert _2=1\}\).

  2. 2.

    CVXOPT: https://cvxopt.org/.

References

  1. Horn BK (1970) Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Technical report

    Google Scholar 

  2. Ikeuchi K, Horn BK (1981) Numerical shape from shading and occluding boundaries. Artif Intell 17(1–3):141–184

    Article  Google Scholar 

  3. Woodham RJ (1980) Photometric method for determining surface orientation from multiple images. Opt Eng 19(1):191139

    Article  Google Scholar 

  4. Ikeuchi K (1981) Determining surface orientations of specular surfaces by using the photometric stereo method. IEEE Trans Pattern Anal Mach Intell (PAMI) 6:661–669

    Article  Google Scholar 

  5. Barsky S, Petrou M (2003) The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Trans Pattern Anal Mach Intell (PAMI) 25(10):1239–1252

    Article  Google Scholar 

  6. Mukaigawa Y, Ishii Y, Shakunaga T (2007) Analysis of photometric factors based on photometric linearization. J Opt Soc Am 24(10):3326–3334

    Article  Google Scholar 

  7. Miyazaki D, Hara K, Ikeuchi K (2010) Median photometric stereo as applied to the segonko tumulus and museum objects. Int J Comput Vis (IJCV) 86(2–3):229

    Article  Google Scholar 

  8. Wu L, Ganesh A, Shi B, Matsushita Y, Wang Y, Ma Y (2010) Robust photometric stereo via low-rank matrix completion and recovery. In: Proceedings of the Asian conference on computer vision (ACCV). Springer, pp 703–717

    Google Scholar 

  9. Ikehata S, Wipf D, Matsushita Y, Aizawa K (2012) Robust photometric stereo using sparse regression. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 318–325

    Google Scholar 

  10. Ikehata S, Wipf D, Matsushita Y, Aizawa K (2014) Photometric stereo using sparse bayesian regression for general diffuse surfaces. IEEE Trans Pattern Anal Mach Intell (PAMI) 36(9):1816–1831

    Article  Google Scholar 

  11. Gentle JE (2007) Matrix algebra: theory, computations, and applications in statistics, 1st edn. Springer Publishing Company, Incorporated

    Google Scholar 

  12. Lawson CL (1961) Contribution to the theory of linear least maximum approximation. PhD thesis, PhD dissertation, UCLA

    Google Scholar 

  13. Gorodnitsky IF, Rao BD (1997) Sparse signal reconstruction from limited data using focuss: a re-weighted minimum norm algorithm. IEEE Trans Image Process (TIP) 45(3):600–616

    Article  Google Scholar 

  14. Samejima M, Matsushita Y (2016) Fast general norm approximation via iteratively reweighted least squares. In: Proceedings of the Asian conference on computer vision (ACCV) workshops. Springer, pp 207–221

    Google Scholar 

  15. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1(Jun):211–244

    Google Scholar 

  16. Wipf DP, Palmer J, Rao BD (2004) Perspectives on sparse Bayesian learning. In: Advances in neural information processing systems, pp 249–256

    Google Scholar 

  17. Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM (JACM) 58(3):1–37

    Article  MathSciNet  Google Scholar 

  18. Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv:1009.5055

  19. Ji S, Ye J (2009) An accelerated gradient method for trace norm minimization. In: Proceedings of the international conference on machine learning. ACM, pp 457–464

    Google Scholar 

  20. Oh TH, Matsushita Y, Tai YW, Kweon IS (2017) Fast randomized singular value thresholding for low-rank optimization. IEEE Trans Pattern Anal Mach Intell (PAMI) 40(2):376–391

    Article  Google Scholar 

  21. Hayakawa H (1994) Photometric stereo under a light source with arbitrary motion. J Opt Soc Am A 11. https://doi.org/10.1364/JOSAA.11.003079

  22. Horn BK, Brooks MJ (1986) The variational approach to shape from shading. Comput Vis Graph Image Process 33(2):174–208

    Article  Google Scholar 

  23. Belhumeur PN, Kriegman DJ, Yuille AL (1999) The bas-relief ambiguity. Int J Comput Vis (IJCV) 35(1):33–44

    Article  Google Scholar 

  24. Cho D, Matsushita Y, Tai YW, Kweon I (2016) Photometric stereo under non-uniform light intensities and exposures. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 170–186

    Google Scholar 

  25. Cho D, Matsushita Y, Tai YW, Kweon IS (2018) Semi-calibrated photometric stereo. IEEE Trans Pattern Anal Mach Intell (PAMI) 42(1):232–245

    Article  Google Scholar 

  26. Horn BK, Ikeuchi K (1984) The mechanical manipulation of randomly oriented parts. Sci Am 251(2):100–113

    Article  Google Scholar 

  27. Hertzmann A, Seitz SM (2005) Example-based photometric stereo: shape reconstruction with general, varying brdfs. IEEE Trans Pattern Anal Mach Intell (PAMI) 27(8):1254–1264

    Article  Google Scholar 

  28. Hui Z, Sankaranarayanan AC (2016) Shape and spatially-varying reflectance estimation from virtual exemplars. IEEE Trans Pattern Anal Mach Intell (PAMI) 39(10):2060–2073

    Article  Google Scholar 

  29. Santo H, Samejima M, Sugano Y, Shi B, Matsushita Y (2017) Deep photometric stereo network. In: Proceedings of the international conference on computer vision (ICCV) workshops, pp 501–509

    Google Scholar 

  30. Chen G, Han K, Wong KYK (2018) Ps-fcn: A flexible learning framework for photometric stereo. In: Proceedings of the European conference on computer vision (ECCV), pp 3–18

    Google Scholar 

  31. Ikehata S (2018) Cnn-ps: Cnn-based photometric stereo for general non-convex surfaces. In: Proceedings of the European conference on computer vision (ECCV), pp 3–18

    Google Scholar 

  32. Taniai T, Maehara T (2018) Neural inverse rendering for general reflectance photometric stereo. In: Proceedings of the international conference on machine learning, pp 4864–4873

    Google Scholar 

  33. Chen G, Han K, Shi B, Matsushita Y, Wong KYK (2019) Self-calibrating deep photometric stereo networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 8739–8747

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasuyuki Matsushita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryusuke Sagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroshi Kawasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yasuhiro Mukaigawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ryo Furukawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daisuke Miyazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katsushi Ikeuchi .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ikeuchi, K. et al. (2020). Photometric Stereo. 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_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-56577-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56576-3

  • Online ISBN: 978-3-030-56577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation