  Although deep neural networks have been widely applied to computer vision
problems, extending them into multiview depth estimation is non-trivial. In
this paper, we present MVDepthNet, a convolutional network to solve the depth
estimation problem given several image-pose pairs from a localized monocular
camera in neighbor viewpoints. Multiview observations are encoded in a cost
volume and then combined with the reference image to estimate the depth map
using an encoder-decoder network. By encoding the information from multiview
observations into the cost volume, our method achieves real-time performance
and the flexibility of traditional methods that can be applied regardless of
the camera intrinsic parameters and the number of images. Geometric data
augmentation is used to train MVDepthNet. We further apply MVDepthNet in a
monocular dense mapping system that continuously estimates depth maps using a
single localized moving camera. Experiments show that our method can generate
depth maps efficiently and precisely.
