Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Indoor Layouts from Simple Point-Clouds   Reconstructing a layout of indoor spaces has been a crucial part of growing
indoor location based services. One of the key challenges in the proliferation
of indoor location based services is the unavailability of indoor spatial maps
due to the complex nature of capturing an indoor space model (e.g., floor plan)
of an existing building. In this paper, we propose a system to automatically
generate floor plans that can recognize rooms from the point-clouds obtained
through smartphones like Google's Tango. In particular, we propose two
approaches - a Recurrent Neural Network based approach using Pointer Network
and a Convolutional Neural Network based approach using Mask-RCNN to identify
rooms (and thereby floor plans) from point-clouds. Experimental results on
different datasets demonstrate approximately 0.80-0.90 Intersection-over-Union
scores, which show that our models can effectively identify the rooms and
regenerate the shapes of the rooms in heterogeneous environment.

5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
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The categories of this paper are: econ
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph