Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AcED: Accurate and Edge-consistent Monocular Depth Estimation   Single image depth estimation is a challenging problem. The current
state-of-the-art method formulates the problem as that of ordinal regression.
However, the formulation is not fully differentiable and depth maps are not
generated in an end-to-end fashion. The method uses a na\"ive threshold
strategy to determine per-pixel depth labels, which results in significant
discretization errors. For the first time, we formulate a fully differentiable
ordinal regression and train the network in end-to-end fashion. This enables us
to include boundary and smoothness constraints in the optimization function,
leading to smooth and edge-consistent depth maps. A novel per-pixel confidence
map computation for depth refinement is also proposed. Extensive evaluation of
the proposed model on challenging benchmarks reveals its superiority over
recent state-of-the-art methods, both quantitatively and qualitatively.
Additionally, we demonstrate practical utility of the proposed method for
single camera bokeh solution using in-house dataset of challenging real-life
images.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th