  In this paper, we address the problem of estimating dense depth from a
sequence of images using deep neural networks. Specifically, we employ a
dense-optical-flow network to compute correspondences and then triangulate the
point cloud to obtain an initial depth map.Parts of the point cloud, however,
may be less accurate than others due to lack of common observations or small
parallax. To further increase the triangulation accuracy, we introduce a
depth-refinement network (DRN) that optimizes the initial depth map based on
the image's contextual cues. In particular, the DRN contains an iterative
refinement module (IRM) that improves the depth accuracy over iterations by
refining the deep features. Lastly, the DRN also predicts the uncertainty in
the refined depths, which is desirable in applications such as measurement
selection for scene reconstruction. We show experimentally that our algorithm
outperforms state-of-the-art approaches in terms of depth accuracy, and verify
that our predicted uncertainty is highly correlated to the actual depth error.
