Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aperture Supervision for Monocular Depth Estimation   We present a novel method to train machine learning algorithms to estimate
scene depths from a single image, by using the information provided by a
camera's aperture as supervision. Prior works use a depth sensor's outputs or
images of the same scene from alternate viewpoints as supervision, while our
method instead uses images from the same viewpoint taken with a varying camera
aperture. To enable learning algorithms to use aperture effects as supervision,
we introduce two differentiable aperture rendering functions that use the input
image and predicted depths to simulate the depth-of-field effects caused by
real camera apertures. We train a monocular depth estimation network end-to-end
to predict the scene depths that best explain these finite aperture images as
defocus-blurred renderings of the input all-in-focus image.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
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The categories of this paper are: nlin
26
The categories of this paper are: bayes-an