Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

Enhanced Andreev reflection in gapped graphene

We theoretically demonstrate unusual features of superconducting proximity effect in gapped graphene which presents a pseudospin symmetry-broken ferromagnet with a net pseudomagnetization. We find that the presence of a band gap makes the Andreev conductance of graphene superconductor/pseudoferromagnet (S/PF) junction to behave similar to that of a graphene ferromagnet-superconductor junction. The energy gap $\Delta_N$ enhance the pseudospin inverted Andreev conductance of S/PF junction to reach a limiting maximum value for $\Delta_N\gg \mu$, which depending on the bias voltage can be larger than the value for the corresponding junction with no energy gap. We further demonstrate a damped-oscillatory behavior for the local density of states of the PF region of S/PF junction and a long-range crossed Andreev reflection process in PF/S/PF structure with antiparallel alignment of pseudomagnetizations of PFs, which confirm that, in this respect, the gapped normal graphene behaves like a ferromagnetic graphene.Comment: 7.2 pages, 5 figures, accepted for publication in Phys. Rev.