Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction   We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from other images.We train the network on 10 million triplets of coplanar and
non-coplanar patches, and evaluate on a new coplanarity benchmark created from
commodity RGB-D scans. Experiments show that our learned descriptor outperforms
alternatives extended for this new task by a significant margin. In addition,
we demonstrate the benefits of coplanarity matching in a robust RGBD
reconstruction formulation.We find that coplanarity constraints detected with
our method are sufficient to get reconstruction results comparable to
state-of-the-art frameworks on most scenes, but outperform other methods on
standard benchmarks when combined with a simple keypoint method.

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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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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: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con