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

Explicit measurement on depth-color inconsistency for depth completion

© 2016 IEEE. Color-guided depth completion is to refine depth map through structure light sensing by filling missing depth structure and de-nosing. It is based on the assumption that depth discontinuity and color edge at the corresponding location are consistent. Among all proposed methods, MRF-based method including its variants is one of major approaches. However, the assumption above is not always true, which causes texture-copy and depth discontinuity blurring artifacts. The state-of-the-art solutions usually are to modify the weighting inside smoothness term of MRF model. Because there is no any method explicitly considering the inconsistency occurring between depth discontinuity and the corresponding color edge, they cannot adaptively control the effect of guidance from color image when completing depth map. In this paper, we propose quantitative measurement on such inconsistency and explicitly embed it into weighting value of smoothness term. The proposed method is evaluated on NYU Kinect datasets and demonstrates promising results

A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201