Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Monocular Image Depth Learning and Confidence Estimation   Convolutional Neural Networks (CNNs) need large amounts of data with ground
truth annotation, which is a challenging problem that has limited the
development and fast deployment of CNNs for many computer vision tasks. We
propose a novel framework for depth estimation from monocular images with
corresponding confidence in a self-supervised manner. A fully differential
patch-based cost function is proposed by using the Zero-Mean Normalized Cross
Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This
approach greatly increases the accuracy and robustness of the depth learning.
In addition, the proposed patch-based cost function can provide a 0 to 1
confidence, which is then used to supervise the training of a parallel network
for confidence map learning and estimation. Evaluation on KITTI dataset shows
that our method outperforms the state-of-the-art results.

4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg