Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interest point detectors stability evaluation on ApolloScape dataset   In the recent years, a number of novel, deep-learning based, interest point
detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However
there's a lack of a standard benchmark to evaluate suitability of these novel
keypoint detectors for real-live applications such as autonomous driving.
Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of
relatively few images of mostly planar scenes taken in favourable conditions.
In this paper we verify if the recent, deep-learning based interest point
detectors have the advantage over the traditional, hand-crafted keypoint
detectors. To this end, we evaluate stability of a number of hand crafted and
recent, learning-based interest point detectors on the street-level view
ApolloScape dataset.

7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc