Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of
  Planes   The task of room layout estimation is to locate the wall-floor, wall-ceiling,
and wall-wall boundaries. Most recent methods solve this problem based on
edge/keypoint detection or semantic segmentation. However, these approaches
have shown limited attention on the geometry of the dominant planes and the
intersection between them, which has significant impact on room layout. In this
work, we propose to incorporate geometric reasoning to deep learning for layout
estimation. Our approach learns to infer the depth maps of the dominant planes
in the scene by predicting the pixel-level surface parameters, and the layout
can be generated by the intersection of the depth maps. Moreover, we present a
new dataset with pixel-level depth annotation of dominant planes. It is larger
than the existing datasets and contains both cuboid and non-cuboid rooms.
Experimental results show that our approach produces considerable performance
gains on both 2D and 3D datasets.

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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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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: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th