We address the depth estimation problem in order to achieve a high-quality 3D reconstruction of the scene. In this
thesis, we focus on two major problems of depth estimation including depth completion from sparse measurements
and depth prediction in multi-view stereo. We propose novel deep learning architectures for these problems. Our
proposals are mainly based on the sequential property of the input such as video or a sequence of images. They
utilize the estimated depths of neighboring views to compensate estimation of the reference view. As a result,
the proposed methods can produce temporal consistent depth. We also propose an uncertainty estimation for the
predicted depth to efficiently remove the outliers when performing 3D reconstruction. Extensive experiments
show that our frameworks achieve significant improvement compared with the state-of-the-art baselines in both
offline and online fashion.
