  Depth-from-defocus (DFD), modeling the relationship between depth and defocus
pattern in images, has demonstrated promising performance in depth estimation.
Recently, several self-supervised works try to overcome the difficulties in
acquiring accurate depth ground-truth. However, they depend on the all-in-focus
(AIF) images, which cannot be captured in real-world scenarios. Such limitation
discourages the applications of DFD methods. To tackle this issue, we propose a
completely self-supervised framework that estimates depth purely from a sparse
focal stack. We show that our framework circumvents the needs for the depth and
AIF image ground-truth, and receives superior predictions, thus closing the gap
between the theoretical success of DFD works and their applications in the real
world. In particular, we propose (i) a more realistic setting for DFD tasks,
where no depth or AIF image ground-truth is available; (ii) a novel
self-supervision framework that provides reliable predictions of depth and AIF
image under the challenging setting. The proposed framework uses a neural model
to predict the depth and AIF image, and utilizes an optical model to validate
and refine the prediction. We verify our framework on three benchmark datasets
with rendered focal stacks and real focal stacks. Qualitative and quantitative
evaluations show that our method provides a strong baseline for self-supervised
DFD tasks.
