Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Evaluation of Learned 3D Features   Matching surfaces is a challenging 3D Computer Vision problem typically
addressed by local features. Although a variety of 3D feature detectors and
descriptors has been proposed in literature, they have seldom been proposed
together and it is yet not clear how to identify the most effective
detector-descriptor pair for a specific application. A promising solution is to
leverage machine learning to learn the optimal 3D detector for any given 3D
descriptor [15]. In this paper, we report a performance evaluation of the
detector-descriptor pairs obtained by learning a paired 3D detector for the
most popular 3D descriptors. In particular, we address experimental settings
dealing with object recognition and surface registration.

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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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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: dg-ga
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio