  We propose a system that uses a convolution neural network (CNN) to estimate
depth from a stereo pair followed by volumetric fusion of the predicted depth
maps to produce a 3D reconstruction of a scene. Our proposed depth refinement
architecture, predicts view-consistent disparity and occlusion maps that helps
the fusion system to produce geometrically consistent reconstructions. We
utilize 3D dilated convolutions in our proposed cost filtering network that
yields better filtering while almost halving the computational cost in
comparison to state of the art cost filtering architectures.For feature
extraction we use the Vortex Pooling architecture. The proposed method achieves
state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo
benchmarks. Finally, we demonstrate that our system is able to produce high
fidelity 3D scene reconstructions that outperforms the state of the art stereo
system.
