15 research outputs found
Vertical Optimizations of Convolutional Neural Networks for Embedded Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Enabling monocular depth perception at the very edge
Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge
Real-time single image depth perception in the wild with handheld devices
Depth perception is paramount to tackle real-world problems, ranging from
autonomous driving to consumer applications. For the latter, depth estimation
from a single image represents the most versatile solution, since a standard
camera is available on almost any handheld device. Nonetheless, two main issues
limit its practical deployment: i) the low reliability when deployed
in-the-wild and ii) the demanding resource requirements to achieve real-time
performance, often not compatible with such devices. Therefore, in this paper,
we deeply investigate these issues showing how they are both addressable
adopting appropriate network design and training strategies -- also outlining
how to map the resulting networks on handheld devices to achieve real-time
performance. Our thorough evaluation highlights the ability of such fast
networks to generalize well to new environments, a crucial feature required to
tackle the extremely varied contexts faced in real applications. Indeed, to
further support this evidence, we report experimental results concerning
real-time depth-aware augmented reality and image blurring with smartphones
in-the-wild.Comment: 11 pages, 9 figure
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