Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Satoshi IkehataKiyoharu Aizawa

Abstract

Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into two categories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertian structure for the inliers, and thus performance degrades when general diffuse regions are present. The second utilizes complex reflectance representations and non-linear optimization over pixels to handle non-Lambertian surfaces, but does not explicitly account for shadows or other forms of corrupting outliers. In this paper, we present a purely pixel-wise photometric stereo method that stably and efficiently handles various non-Lambertian effects by assuming that appearances can be decomposed into a sparse, non-diffuse component (e.g., shadows, specularities, etc.) and a diffuse component represented by a monotonic function of the surface normal and lighting dot-product. This function is constructed using a piecewise linear approximation to the inverse diffuse model, leading to closed-form estimates of the surface normals and model parameters in the absence of non-diffuse corruptions. The latter are modeled as latent variables embedded within a hierarchical Bayesian model such that we may accurately compute the unknown s...Continue Reading

References

Aug 27, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Aaron Hertzmann, Steven M Seitz
Oct 4, 2007·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Yasuhiro MukaigawaTakeshi Shakunaga
Jan 16, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Carlos Hernández EstebanRoberto Cipolla
Feb 23, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ping TanTodd Zickler
Dec 14, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Alessio Del BueMarco Paladini

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Citations

Aug 25, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Tian-Qi Han, Hui-Liang Shen
Nov 17, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Hui-Liang Shen Chunguang Li

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