Accurate Polarimetric BRDF for Real Polarization Scene Rendering
Polarization has been used to solve a lot of computer vision tasks such as Shape from Polarization (SfP). But existing methods suffer from ambiguity problems of polarization. To overcome such problems, some research works have suggested to use Convolutional Neural Network (CNN). But acquiring large scale dataset with polarization information is a very difficult task. If there is an accurate model which can describe a complicated phenomenon of polarization, we can easily produce synthetic polarized images with various situations to train CNN. In this paper, we propose a new polarimetric BRDF (pBRDF) model. We prove its accuracy by fitting our model to measured data with variety of light and camera conditions. We render polarized images using this model and use them to estimate surface normal. Experiments show that the CNN trained by our polarized images has more accuracy than one trained by RGB only.
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