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, highresolution estimate from a noisy, low-resolution input depth map. Additionally, a highresolution 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.
Riegler et al. REF proposed a depth image SR reconstruction method based on deep primal-dual networks, which combines a deep fully convolutional network with a non-local variation.
A Deep Primal-Dual Network for Guided Depth Super-Resolution
ArXiv
ArXiv
Computer Science