  Focus based methods have shown promising results for the task of depth
estimation. However, most existing focus based depth estimation approaches
depend on maximal sharpness of the focal stack. Out of focus information in the
focal stack poses challenges for this task. In this paper, we propose a
dynamically multi modal learning strategy which incorporates RGB data and the
focal stack in our framework. Our goal is to deeply excavate the spatial
correlation in the focal stack by designing the spatial correlation perception
module and dynamically fuse multi modal information between RGB data and the
focal stack in a adaptive way by designing the multi modal dynamic fusion
module. The success of our method is demonstrated by achieving the state of the
art performance on two datasets. Furthermore, we test our network on a set of
different focused images generated by a smart phone camera to prove that the
proposed method not only broke the limitation of only using light field data,
but also open a path toward practical applications of depth estimation on
common consumer level cameras data.
