Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aggregation of binary feature descriptors for compact scene model
  representation in large scale structure-from-motion applications   In this paper we present an efficient method for aggregating binary feature
descriptors to allow compact representation of 3D scene model in incremental
structure-from-motion and SLAM applications. All feature descriptors linked
with one 3D scene point or landmark are represented by a single low-dimensional
real-valued vector called a \emph{prototype}. The method allows significant
reduction of memory required to store and process feature descriptors in
large-scale structure-from-motion applications. An efficient approximate
nearest neighbours search methods suited for real-valued descriptors, such as
FLANN, can be used on the resulting prototypes to speed up matching processed
frames.

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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: eess
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat