  We present DeepMVS, a deep convolutional neural network (ConvNet) for
multi-view stereo reconstruction. Taking an arbitrary number of posed images as
input, we first produce a set of plane-sweep volumes and use the proposed
DeepMVS network to predict high-quality disparity maps. The key contributions
that enable these results are (1) supervised pretraining on a photorealistic
synthetic dataset, (2) an effective method for aggregating information across a
set of unordered images, and (3) integrating multi-layer feature activations
from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using
the ETH3D Benchmark. Our results show that DeepMVS compares favorably against
state-of-the-art conventional MVS algorithms and other ConvNet based methods,
particularly for near-textureless regions and thin structures.
