  We propose DeepV2D, an end-to-end deep learning architecture for predicting
depth from video. DeepV2D combines the representation ability of neural
networks with the geometric principles governing image formation. We compose a
collection of classical geometric algorithms, which are converted into
trainable modules and combined into an end-to-end differentiable architecture.
DeepV2D interleaves two stages: motion estimation and depth estimation. During
inference, motion and depth estimation are alternated and converge to accurate
depth. Code is available https://github.com/princeton-vl/DeepV2D.
