2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Oct. 22, 2017 to Oct. 29, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.250
In this paper we present a differential approach to photo-polarimetric shape estimation. We propose several alternative differential constraints based on polarisation and photometric shading information and show how to express them in a unified partial differential system. Our method uses the image ratios technique to combine shading and polarisation information in order to directly reconstruct surface height, without first computing surface normal vectors. Moreover, we are able to remove the non-linearities so that the problem reduces to solving a linear differential problem. We also introduce a new method for estimating a polarisation image from multichannel data and, finally, we show it is possible to estimate the illumination directions in a two source setup, extending the method into an uncalibrated scenario. From a numerical point of view, we use a least-squares formulation of the discrete version of the problem. To the best of our knowledge, this is the first work to consider a unified differential approach to solve photo-polarimetric shape estimation directly for height. Numerical results on synthetic and real-world data confirm the effectiveness of our proposed method.
image reconstruction, least squares approximations, partial differential equations, photometry, polarimetry
S. Tozza, W. A. Smith, D. Zhu, R. Ramamoorthi and E. R. Hancock, "Linear Differential Constraints for Photo-Polarimetric Height Estimation," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2018, pp. 2298-2306.