  Monocular depth estimation is still an open challenge due to the ill-posed
nature of the problem at hand. Deep learning based techniques have been
extensively studied and proved capable of producing acceptable depth estimation
accuracy even if the lack of meaningful and robust depth cues within single RGB
input images severally limits their performance. Coded aperture-based methods
using phase and amplitude masks encode strong depth cues within 2D images by
means of depth-dependent Point Spread Functions (PSFs) at the price of a
reduced image quality. In this paper, we propose a novel end-to-end learning
approach for depth from diffracted rotation. A phase mask that produces a
Rotating Point Spread Function (RPSF) as a function of defocus is jointly
optimized with the weights of a depth estimation neural network. To this aim,
we introduce a differentiable physical model of the aperture mask and exploit
an accurate simulation of the camera imaging pipeline. Our approach requires a
significantly less complex model and less training data, yet it is superior to
existing methods in the task of monocular depth estimation on indoor
benchmarks. In addition, we address the problem of image degradation by
incorporating a non-blind and non-uniform image deblurring module to recover
the sharp all-in-focus image from its RPSF-blurred counterpart.
