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.

High-mobility organic transistors are fabricated on both surfaces of approximately 1-μm-thick rubrene crystals, molecularly flat over an area of 10×10μm2. A thin platelet of 9,10-diphenylanthracene single crystal and surface-passivated SiO2 are used for the gate insulators. Because of the minimized densities of hole-trapping levels at the interfaces and in the rubrene crystal, the field-induced carriers do not necessarily reside near the interface but are distributed in the bulk of the semiconductor by adjusting the two gate voltages. Making use of the highly mobile carriers in the inner crystal, the mobility is maximized to ∼43cm2∕Vs.

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 surface normals while simultaneously separating diffuse from non-diffuse components. Extensive evaluations are performed that show state-of-the-art performance using both synthetic and real-world images.

High-density carrier accumulation in organic semiconductors is demonstrated in Au∕polymer gel electrolyte/rubrene crystal∕SiO2∕doped Si dual-gate transistors, forming electric double layers in the polymer gel. Application of only 1.2V across the polymer gel electrolyte drastically enhances the conductance of the rubrene single crystal with the field-induced carrier density up to ∼5×1013cm−2. Directly comparing the transfer characteristics of the same device channel in the dual-gate transistors revealed that the achieved doping level is beyond the maximum of the SiO2-based transistor on the opposite side of the organic crystal.