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.

本文提出了一种新的方法来提高深度图像的空间分辨率，使用深度全卷积神经网络和非局部变分方法在深度的原始 - 对偶网络中结合起来，通过训练我们的联合方法，可以优化卷积网络和变分方法及其优化过程等所有参数。评估表明，该方法在多个基准测试中性能优于现有技术。