  Depth estimation from focal stacks is a fundamental computer vision problem
that aims to infer depth from focus/defocus cues in the image stacks. Most
existing methods tackle this problem by applying convolutional neural networks
(CNNs) with 2D or 3D convolutions over a set of fixed stack images to learn
features across images and stacks. Their performance is restricted due to the
local properties of the CNNs, and they are constrained to process a fixed
number of stacks consistent in train and inference, limiting the generalization
to the arbitrary length of stacks. To handle the above limitations, we develop
a novel Transformer-based network, FocDepthFormer, composed mainly of a
Transformer with an LSTM module and a CNN decoder. The self-attention in
Transformer enables learning more informative features via an implicit
non-local cross reference. The LSTM module is learned to integrate the
representations across the stack with arbitrary images. To directly capture the
low-level features of various degrees of focus/defocus, we propose to use
multi-scale convolutional kernels in an early-stage encoder. Benefiting from
the design with LSTM, our FocDepthFormer can be pre-trained with abundant
monocular RGB depth estimation data for visual pattern capturing, alleviating
the demand for the hard-to-collect focal stack data. Extensive experiments on
various focal stack benchmark datasets show that our model outperforms the
state-of-the-art models on multiple metrics.
