  Light field data has been demonstrated to facilitate the depth estimation
task. Most learning-based methods estimate the depth infor-mation from EPI or
sub-aperture images, while less methods pay attention to the focal stack.
Existing learning-based depth estimation methods from the focal stack lead to
suboptimal performance because of the defocus blur. In this paper, we propose a
multi-modal learning method for robust light field depth estimation. We first
excavate the internal spatial correlation by designing a context reasoning unit
which separately extracts comprehensive contextual information from the focal
stack and RGB images. Then we integrate the contextual information by
exploiting a attention-guide cross-modal fusion module. Extensive experiments
demonstrate that our method achieves superior performance than existing
representative methods on two light field datasets. Moreover, visual results on
a mobile phone dataset show that our method can be widely used in daily life.
