Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Self-Contradiction to Learn Confidence Measures in Stereo Vision   Learned confidence measures gain increasing importance for outlier removal
and quality improvement in stereo vision. However, acquiring the necessary
training data is typically a tedious and time consuming task that involves
manual interaction, active sensing devices and/or synthetic scenes. To overcome
this problem, we propose a new, flexible, and scalable way for generating
training data that only requires a set of stereo images as input. The key idea
of our approach is to use different view points for reasoning about
contradictions and consistencies between multiple depth maps generated with the
same stereo algorithm. This enables us to generate a huge amount of training
data in a fully automated manner. Among other experiments, we demonstrate the
potential of our approach by boosting the performance of three learned
confidence measures on the KITTI2012 dataset by simply training them on a vast
amount of automatically generated training data rather than a limited amount of
laser ground truth data.

12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con