Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints   Learned local descriptors based on Convolutional Neural Networks (CNNs) have
achieved significant improvements on patch-based benchmarks, whereas not having
demonstrated strong generalization ability on recent benchmarks of image-based
3D reconstruction. In this paper, we mitigate this limitation by proposing a
novel local descriptor learning approach that integrates geometry constraints
from multi-view reconstructions, which benefits the learning process in terms
of data generation, data sampling and loss computation. We refer to the
proposed descriptor as GeoDesc, and demonstrate its superior performance on
various large-scale benchmarks, and in particular show its great success on
challenging reconstruction tasks. Moreover, we provide guidelines towards
practical integration of learned descriptors in Structure-from-Motion (SfM)
pipelines, showing the good trade-off that GeoDesc delivers to 3D
reconstruction tasks between accuracy and efficiency.

18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex