  Depth-from-focus (DFF) is a technique that infers depth using the focus
change of a camera. In this work, we propose a convolutional neural network
(CNN) to find the best-focused pixels in a focal stack and infer depth from the
focus estimation. The key innovation of the network is the novel deep
differential focus volume (DFV). By computing the first-order derivative with
the stacked features over different focal distances, DFV is able to capture
both the focus and context information for focus analysis. Besides, we also
introduce a probability regression mechanism for focus estimation to handle
sparsely sampled focal stacks and provide uncertainty estimation to the final
prediction. Comprehensive experiments demonstrate that the proposed model
achieves state-of-the-art performance on multiple datasets with good
generalizability and fast speed.
