Abstract
Photometric stereo is a technique of surface reconstruction using several object images made with a fixed camera position and varying illumination directions. Reconstructed surfaces can have complex reflecting properties which are unknown a priori and often simplified by Lambertian model (reflecting light uniformly in all directions). Such simplification leads to certain inaccuracy of reconstruction but in most cases is sufficient to obtain general object relief important for further recognition. Not only surface properties but also lighting sources utilized for each image acquisition can be very complex for modeling, or even unknown. Our work demonstrates how to find surface normals from Lambertian photometric stereo model using color images made with a priori unknown lighting directions. Evaluation of model components is based on an alternating optimization approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Optical Engineering 19, 513–531 (1980)
Horn, B.K.P.: Robot Vision. MIT Press (1986)
Hertzmann, A., Seitz, S.M.: Example-based photometric stereo: shape reconstruction with general, varying BRDFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1254–1263 (2005)
Hayakawa, H.: Photometric stereo under a light source with arbitrary motion. Optical Society of America 11, 3079–3088 (1994)
Basri, R., Jacobs, D.W., Kemelmacher, I.: Photometric stereo with general, unknown lighting. International Journal of Computer Vision 72, 239–257 (2007)
Coleman, E.N., Jain, R.: Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry. Computer Graphics and Image Processing 18, 309–328 (1982)
Barsky, S., Petrou, M.: The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1239–1252 (2003)
Biswas, S., Aggarwal, G., Chellappa, R.: Robust estimation of albedo for illumination-invariant matching and shape recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence 5, 884–899 (2009)
Miyazaki, D., Ikeuchi, K.: Photometric stereo under unknown light sources using robust SVD with missing data. In: Proceedings of the IEEE International Conference on Image Processing, pp. 4057–4060 (2010)
Belhumeur, P.N., Kreigman, D.J., Yuille, A.L.: The bas-relief ambiguity. International Journal of Computer Vision 35, 33–44 (1999)
Csiszár, I., Tusnády, G.: Information geometry and alternating minimization procedures. Statistics & Decisions (suppl. 1), 205–237 (1984)
Hathaway, R.J., Hu, Y., Bezdek, J.C.: Local convergence of tri-level alternating optimization. Neural, Parallel & Scientific Computation 9, 19–28 (2001)
Brooks, M.J., Horn, B.K.P.: Shape and source from shading. Technical report, MIT (1985)
Lawson, C.L., Hanson, R.J.: Solving least squares problems. Prentice-Hall (1974)
Grosse, R., Johnson, M.K., Adelson, E.H., Freeman, W.T.: Ground-truth dataset and baseline evaluations for intrinsic image algorithms. In: Proceedings of the International Conference on Computer Vision, pp. 2335–2342 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyrgyzova, K., Allano, L., Aupetit, M. (2013). Alternating Optimization for Lambertian Photometric Stereo Model with Unknown Lighting Directions. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-40246-3_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40245-6
Online ISBN: 978-3-642-40246-3
eBook Packages: Computer ScienceComputer Science (R0)