12,909 research outputs found
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
Depth Estimation Through a Generative Model of Light Field Synthesis
Light field photography captures rich structural information that may
facilitate a number of traditional image processing and computer vision tasks.
A crucial ingredient in such endeavors is accurate depth recovery. We present a
novel framework that allows the recovery of a high quality continuous depth map
from light field data. To this end we propose a generative model of a light
field that is fully parametrized by its corresponding depth map. The model
allows for the integration of powerful regularization techniques such as a
non-local means prior, facilitating accurate depth map estimation.Comment: German Conference on Pattern Recognition (GCPR) 201
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