Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Depth From Focus   Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.

16
The categories of this paper are: stat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th