Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning structure-from-motion from motion   This work is based on a questioning of the quality metrics used by deep
neural networks performing depth prediction from a single image, and then of
the usability of recently published works on unsupervised learning of depth
from videos. To overcome their limitations, we propose to learn in the same
unsupervised manner a depth map inference system from monocular videos that
takes a pair of images as input. This algorithm actually learns
structure-from-motion from motion, and not only structure from context
appearance. The scale factor issue is explicitly treated, and the absolute
depth map can be estimated from camera displacement magnitude, which can be
easily measured from cheap external sensors. Our solution is also much more
robust with respect to domain variation and adaptation via fine tuning, because
it does not rely entirely in depth from context. Two use cases are considered,
unstabilized moving camera videos, and stabilized ones. This choice is
motivated by the UAV (for Unmanned Aerial Vehicle) use case that generally
provides reliable orientation measurement. We provide a set of experiments
showing that, used in real conditions where only speed can be known, our
network outperforms competitors for most depth quality measures. Results are
given on the well known KITTI dataset, which provides robust stabilization for
our second use case, but also contains moving scenes which are very typical of
the in-car road context. We then present results on a synthetic dataset that we
believe to be more representative of typical UAV scenes. Lastly, we present two
domain adaptation use cases showing superior robustness of our method compared
to single view depth algorithms, which indicates that it is better suited for
highly variable visual contexts.

23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph