Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantics-Driven Unsupervised Learning for Monocular Depth and
  Ego-Motion Estimation   We propose a semantics-driven unsupervised learning approach for monocular
depth and ego-motion estimation from videos in this paper. Recent unsupervised
learning methods employ photometric errors between synthetic view and actual
image as a supervision signal for training. In our method, we exploit semantic
segmentation information to mitigate the effects of dynamic objects and
occlusions in the scene, and to improve depth prediction performance by
considering the correlation between depth and semantics. To avoid costly
labeling process, we use noisy semantic segmentation results obtained by a
pre-trained semantic segmentation network. In addition, we minimize the
position error between the corresponding points of adjacent frames to utilize
3D spatial information. Experimental results on the KITTI dataset show that our
method achieves good performance in both depth and ego-motion estimation tasks.

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The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
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The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
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The categories of this paper are: math-ph