Skip to main content
SpringerLink
Log in
Book cover

Science and Information Conference

CVC 2019: Advances in Computer Vision pp 619–637Cite as

Specular Photometric Stereo for Surface Normal Estimation of Dark Surfaces

  • Conference paper
  • First Online:

  • 2237 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

Abstract

This paper presents Specular Photometric Stereo (SPS), which is a Photometric Stereo (PS) technique incorporating specular reflection. The proposed SPS uses multiple images of a surface under different lighting conditions to obtain surface normals similarly to the conventional PS, but uniquely utilizes specular components of a dark surface, which reflects little diffuse light. The proposed framework consists of two sequential numerical steps, which are the conversion of a highly non-linear specular reflection model to a non-linear equation with only one non-linear parameter, and then the iterative removal of the diffuse components. The proposed SPS can estimate normals of dark surfaces, which is not possible by the conventional PS. The proposed SPS was examined using synthesized data and then tested with real-world surfaces. The results of surface normal estimation show that the capability of the proposed SPS over the existing PS in both accuracy and computational cost.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

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

    Article  Google Scholar 

  2. Li, B., Furukawa, T.: Microtexture road profiling system using photometric stereo. Tire Sci. Technol. 43(2), 117–143 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  4. Li, B., Furukawa, T.: Photometric stereo under dichromatic reflectance framework dealing with non-lambertian surfaces. In: 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 139–144. IEEE (2015)

    Google Scholar 

  5. Kipman, Y.: Non-contact vision based inspection system for flat specular parts. US Patent 6,525,810, 25 February 2003

    Google Scholar 

  6. Klijn, S., Reus, N.J., van der Sommen, C.M., Sicam, V.A.D.P.: Accuracy of a novel specular reflection technique for measurement of total corneal astigmatism. Invest. Ophthalmol. Vis. Sci. 56(7), 1903–1903 (2015)

    Google Scholar 

  7. Blake, A., Brelstaff, G.: Specular stereo. IJCAI 2, 973–976 (1985)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Alldrin, N., Zickler, T., Kriegman, D.: Photometric stereo with non-parametric and spatially-varying reflectance (2008)

    Google Scholar 

  11. Ma, W.-C., Hawkins, T., Peers, P., Chabert, C.-F., Weiss, M., Debevec, P.: Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. In: Proceedings of the 18th Eurographics Conference on Rendering Techniques, pp. 183–194. Eurographics Association (2007)

    Google Scholar 

  12. 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 

  13. Blinn, J.F.: Models of light reflection for computer synthesized pictures. ACM SIGGRAPH Comput. Graph. 11, 192–198 (1977)

    Article  Google Scholar 

  14. Shen, Y.-Q., Ypma, T.J.: Solving nonlinear systems of equations with only one nonlinear variable. J. Comput. Appl. Math. 30(2), 235–246 (1990)

    Article  MathSciNet  Google Scholar 

  15. Kay, G., Caelli, T.: Estimating the parameters of an illumination model using photometric stereo. Graph. Models Image Process. 57(5), 365–388 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA

    Mengyu Song & Tomonari Furukawa

Authors
  1. Mengyu Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomonari Furukawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mengyu Song .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Song, M., Furukawa, T. (2020). Specular Photometric Stereo for Surface Normal Estimation of Dark Surfaces. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_50

Download citation

Publish with us

Policies and ethics

Search

Navigation