4,689 research outputs found
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Stereo matching is one of the longest-standing problems in computer vision
with close to 40 years of studies and research. Throughout the years the
paradigm has shifted from local, pixel-level decision to various forms of
discrete and continuous optimization to data-driven, learning-based methods.
Recently, the rise of machine learning and the rapid proliferation of deep
learning enhanced stereo matching with new exciting trends and applications
unthinkable until a few years ago. Interestingly, the relationship between
these two worlds is two-way. While machine, and especially deep, learning
advanced the state-of-the-art in stereo matching, stereo itself enabled new
ground-breaking methodologies such as self-supervised monocular depth
estimation based on deep networks. In this paper, we review recent research in
the field of learning-based depth estimation from single and binocular images
highlighting the synergies, the successes achieved so far and the open
challenges the community is going to face in the immediate future.Comment: Accepted to TPAMI. Paper version of our CVPR 2019 tutorial:
"Learning-based depth estimation from stereo and monocular images: successes,
limitations and future challenges"
(https://sites.google.com/view/cvpr-2019-depth-from-image/home
Error Correction for Dense Semantic Image Labeling
Pixelwise semantic image labeling is an important, yet challenging, task with
many applications. Typical approaches to tackle this problem involve either the
training of deep networks on vast amounts of images to directly infer the
labels or the use of probabilistic graphical models to jointly model the
dependencies of the input (i.e. images) and output (i.e. labels). Yet, the
former approaches do not capture the structure of the output labels, which is
crucial for the performance of dense labeling, and the latter rely on carefully
hand-designed priors that require costly parameter tuning via optimization
techniques, which in turn leads to long inference times. To alleviate these
restrictions, we explore how to arrive at dense semantic pixel labels given
both the input image and an initial estimate of the output labels. We propose a
parallel architecture that: 1) exploits the context information through a
LabelPropagation network to propagate correct labels from nearby pixels to
improve the object boundaries, 2) uses a LabelReplacement network to directly
replace possibly erroneous, initial labels with new ones, and 3) combines the
different intermediate results via a Fusion network to obtain the final
per-pixel label. We experimentally validate our approach on two different
datasets for the semantic segmentation and face parsing tasks respectively,
where we show improvements over the state-of-the-art. We also provide both a
quantitative and qualitative analysis of the generated results
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