Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Dense 3D Semantic Mapping from Monocular SLAM   The bundle of geometry and appearance in computer vision has proven to be a
promising solution for robots across a wide variety of applications. Stereo
cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and
trajectory tracking in a dense way. However, they lack flexibility of seamless
switch between different scaled environments, i.e., indoor and outdoor scenes.
In addition, semantic information are still hard to acquire in a 3D mapping. We
address this challenge by combining the state-of-art deep learning method and
semi-dense Simultaneous Localisation and Mapping (SLAM) based on video stream
from a monocular camera. In our approach, 2D semantic information are
transferred to 3D mapping via correspondence between connective Keyframes with
spatial consistency. There is no need to obtain a semantic segmentation for
each frame in a sequence, so that it could achieve a reasonable computation
time. We evaluate our method on indoor/outdoor datasets and lead to an
improvement in the 2D semantic labelling over baseline single frame
predictions.

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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int