Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised
  Monocular Depth Prediction   Supervised learning methods to infer (hypothesize) depth of a scene from a
single image require costly per-pixel ground-truth. We follow a geometric
approach that exploits abundant stereo imagery to learn a model to hypothesize
scene structure without direct supervision. Although we train a network with
stereo pairs, we only require a single image at test time to hypothesize
disparity or depth. We propose a novel objective function that exploits the
bilateral cyclic relationship between the left and right disparities and we
introduce an adaptive regularization scheme that allows the network to handle
both the co-visible and occluded regions in a stereo pair. This process
ultimately produces a model to generate hypotheses for the 3-dimensional
structure of the scene as viewed in a single image. When used to generate a
single (most probable) estimate of depth, our method outperforms
state-of-the-art unsupervised monocular depth prediction methods on the KITTI
benchmarks. We show that our method generalizes well by applying our models
trained on KITTI to the Make3d dataset.

13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math