Estimating depth from a single RGB images is a fundamental task in computer
vision, which is most directly solved using supervised deep learning. In the
field of unsupervised learning of depth from a single RGB image, depth is not
given explicitly. Existing work in the field receives either a stereo pair, a
monocular video, or multiple views, and, using losses that are based on
structure-from-motion, trains a depth estimation network. In this work, we
rely, instead of different views, on depth from focus cues. Learning is based
on a novel Point Spread Function convolutional layer, which applies location
specific kernels that arise from the Circle-Of-Confusion in each image
location. We evaluate our method on data derived from five common datasets for
depth estimation and lightfield images, and present results that are on par
with supervised methods on KITTI and Make3D datasets and outperform
unsupervised learning approaches. Since the phenomenon of depth from defocus is
not dataset specific, we hypothesize that learning based on it would overfit
less to the specific content in each dataset. Our experiments show that this is
indeed the case, and an estimator learned on one dataset using our method
provides better results on other datasets, than the directly supervised
methods.

使用深度焦点提示为基础，基于点扩散函数卷积层的无监督学习深度估计方法能在多个数据集上优于其他无监督方法和与 KITTI 和 Make3D 数据集中的监督方法效果相当，并且与数据集无关.