  We introduce a learning-based depth map fusion framework that accepts a set
of depth and confidence maps generated by a Multi-View Stereo (MVS) algorithm
as input and improves them. This is accomplished by integrating volumetric
visibility constraints that encode long-range surface relationships across
different views into an end-to-end trainable architecture. We also introduce a
depth search window estimation sub-network trained jointly with the larger
fusion sub-network to reduce the depth hypothesis search space along each ray.
Our method learns to model depth consensus and violations of visibility
constraints directly from the data; effectively removing the necessity of
fine-tuning fusion parameters. Extensive experiments on MVS datasets show
substantial improvements in the accuracy of the output fused depth and
confidence maps.
