3,030 research outputs found
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included.
Project page:
http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the
sensors of choice for depth perception as they are typically cheaper and more
versatile than their active counterparts. Their biggest drawback, however, is
that they do not directly sense depth maps; instead, these must be estimated
through data-intensive processes. Therefore, appropriate algorithm selection
plays an important role in achieving the desired performance characteristics.
Motivated by applications in space and mobile robotics, we implement and
evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering
one of the best trade-offs between efficiency and accuracy, ELAS has only been
shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all
intriguing properties of the original algorithm, such as the slanted plane
priors, but can achieve a frame rate of 47fps whilst consuming under 4W of
power. Unlike previous FPGA based designs, we take advantage of both components
on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate
more complex and computationally diverse algorithms for such low power,
real-time systems.Comment: 8 pages, 7 figures, 2 table
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
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Stereo matching is one of the most popular techniques to estimate dense depth
maps by finding the disparity between matching pixels on two, synchronized and
rectified images. Alongside with the development of more accurate algorithms,
the research community focused on finding good strategies to estimate the
reliability, i.e. the confidence, of estimated disparity maps. This information
proves to be a powerful cue to naively find wrong matches as well as to improve
the overall effectiveness of a variety of stereo algorithms according to
different strategies. In this paper, we review more than ten years of
developments in the field of confidence estimation for stereo matching. We
extensively discuss and evaluate existing confidence measures and their
variants, from hand-crafted ones to the most recent, state-of-the-art learning
based methods. We study the different behaviors of each measure when applied to
a pool of different stereo algorithms and, for the first time in literature,
when paired with a state-of-the-art deep stereo network. Our experiments,
carried out on five different standard datasets, provide a comprehensive
overview of the field, highlighting in particular both strengths and
limitations of learning-based strategies.Comment: TPAMI final versio
Recommended from our members
Disaster management in Pakistan
This is the accepted manuscript.The final version is available from ICE Publishing via http://dx.doi.org/10.1680/muen.15.00002. Pakistan's largest metropolis and economic hub, Karachi, surrounded by numerous tectonically active faults, is ill-equipped to cope with seismic hazards. The city is vulnerable mainly due to inadequate construction techniques, lack of awareness, political will and scant historical seismic data. The existing disaster management policies often remain ineffective or unimplemented due to technical or financial constraints, shortage of trained personnel and weak information-sharing mechanisms. This research identifies such challenges in Karachi and explores the global disaster risk reduction initiatives from better prepared countries, whose implementation feasibility is assessed based on local economic, social, topographical and political circumstances. Shortlisting five fundamental aspects of disaster risk reduction, a multi-prong strategy is devised, addressing Karachi's vulnerability and exposure to both urban communities and slums. An earthquake early warning system model using cell broadcast technology is formulated, proposing to supplement the existing upgradable seismic network in Pakistan. This and other priority measures constituting the proposed strategy present a customised pragmatic approach of shifting from an emergency response paradigm towards prevention, mitigation and preparedness. </jats:p
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