Most conventional photometric stereo algorithms inversely solve a BRDF-based image formation model. However, the actual imaging process is often far more complex due to the global light transport on the non-convex surfaces. This paper presents a photometric stereo network that directly learns relationships between the photometric stereo input and surface normals of a scene. For handling unordered, arbitrary number of input images, we merge all the input data to the intermediate representation called observation map that has a fixed shape, is able to be fed into a CNN. To improve both training and prediction, we take into account the rotational pseudo-invariance of the observation map that is derived from the isotropic constraint. For training the network, we create a synthetic photometric stereo dataset that is generated by a physics-based renderer, therefore the global light transport is considered. Our experimental results on both synthetic and real datasets show that our method outperforms conventional BRDF-based photometric stereo algorithms especially when scenes are highly non-convex.

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Gate-voltage dependence of carrier mobility is measured in high-performance field-effect transistors of rubrene single crystals by simultaneous detection of the longitudinal conductivity sigma(square) and Hall coefficient R(H). The Hall mobility mu(H) (identical with sigma(square)R(H)) reaches nearly 10 cm(2)/V s when relatively low-density carriers (<10(11) cm(-2)) distribute into the crystal. mu(H) rapidly decreases with higher-density carriers as they are essentially confined to the surface and are subjected to randomness of the amorphous gate insulators. The mechanism to realize high carrier mobility in the organic transistor devices involves intrinsic-semiconductor character of the high-purity organic crystals and diffusive bandlike carrier transport in the bulk.

This paper presents a photometric stereo method that is purely pixelwise and handles general isotropic surfaces in a stable manner. Following the recently proposed sumof-lobes representation of the isotropic reflectance function, we constructed a constrained bivariate regression problem where the regression function is approximated by smooth, bivariate Bernstein polynomials. The unknown normal vector was separated from the unknown reflectance function by considering the inverse representation of the image formation process, and then we could accurately compute the unknown surface normals by solving a simple and efficient quadratic programming problem. Extensive evaluations that showed the state-of-the-art performance using both synthetic and real-world images were performed.