  We present an algorithm for estimating consistent dense depth maps and camera
poses from a monocular video. We integrate a learning-based depth prior, in the
form of a convolutional neural network trained for single-image depth
estimation, with geometric optimization, to estimate a smooth camera trajectory
as well as detailed and stable depth reconstruction. Our algorithm combines two
complementary techniques: (1) flexible deformation-splines for low-frequency
large-scale alignment and (2) geometry-aware depth filtering for high-frequency
alignment of fine depth details. In contrast to prior approaches, our method
does not require camera poses as input and achieves robust reconstruction for
challenging hand-held cell phone captures containing a significant amount of
noise, shake, motion blur, and rolling shutter deformations. Our method
quantitatively outperforms state-of-the-arts on the Sintel benchmark for both
depth and pose estimations and attains favorable qualitative results across
diverse wild datasets.

Computer Vision and Pattern Recognition