  Multi-view stereo (MVS) is the golden mean between the accuracy of active
depth sensing and the practicality of monocular depth estimation. Cost volume
based approaches employing 3D convolutional neural networks (CNNs) have
considerably improved the accuracy of MVS systems. However, this accuracy comes
at a high computational cost which impedes practical adoption. Distinct from
cost volume approaches, we propose an efficient depth estimation approach by
first (a) detecting and evaluating descriptors for interest points, then (b)
learning to match and triangulate a small set of interest points, and finally
(c) densifying this sparse set of 3D points using CNNs. An end-to-end network
efficiently performs all three steps within a deep learning framework and
trained with intermediate 2D image and 3D geometric supervision, along with
depth supervision. Crucially, our first step complements pose estimation using
interest point detection and descriptor learning. We demonstrate
state-of-the-art results on depth estimation with lower compute for different
scene lengths. Furthermore, our method generalizes to newer environments and
the descriptors output by our network compare favorably to strong baselines.
Code is available at https://github.com/magicleap/DELTAS
