Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Backtracking Regression Forests for Accurate Camera Relocalization   Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure, and loop
closure detection. Recent random forests based methods directly predict 3D
world locations for 2D image locations to guide the camera pose optimization.
During training, each tree greedily splits the samples to minimize the spatial
variance. However, these greedy splits often produce uneven sub-trees in
training or incorrect 2D-3D correspondences in testing. To address these
problems, we propose a sample-balanced objective to encourage equal numbers of
samples in the left and right sub-trees, and a novel backtracking scheme to
remedy the incorrect 2D-3D correspondence predictions. Furthermore, we extend
the regression forests based methods to use local features in both training and
testing stages for outdoor RGB-only applications. Experimental results on
publicly available indoor and outdoor datasets demonstrate the efficacy of our
approach, which shows superior or on-par accuracy with several state-of-the-art
methods.

14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg