  Monocular depth estimation is an important step in many downstream tasks in
machine vision. We address the topic of estimating monocular depth from defocus
blur which can yield more accurate results than the semantic based depth
estimation methods. The existing monocular depth from defocus techniques are
sensitive to the particular camera that the images are taken from. We show how
several camera-related parameters affect the defocus blur using optical physics
equations and how they make the defocus blur depend on these parameters. The
simple correction procedure we propose can alleviate this problem which does
not require any retraining of the original model. We created a synthetic
dataset which can be used to test the camera independent performance of depth
from defocus blur models. We evaluate our model on both synthetic and real
datasets (DDFF12 and NYU depth V2) obtained with different cameras and show
that our methods are significantly more robust to the changes of cameras. Code:
https://github.com/sleekEagle/defocus_camind.git
