  To reconstruct a 3D scene from a set of calibrated views, traditional
multi-view stereo techniques rely on two distinct stages: local depth maps
computation and global depth maps fusion. Recent studies concentrate on deep
neural architectures for depth estimation by using conventional depth fusion
method or direct 3D reconstruction network by regressing Truncated Signed
Distance Function (TSDF). In this paper, we advocate that replicating the
traditional two stages framework with deep neural networks improves both the
interpretability and the accuracy of the results. As mentioned, our network
operates in two steps: 1) the local computation of the local depth maps with a
deep MVS technique, and, 2) the depth maps and images' features fusion to build
a single TSDF volume. In order to improve the matching performance between
images acquired from very different viewpoints (e.g., large-baseline and
rotations), we introduce a rotation-invariant 3D convolution kernel called
PosedConv. The effectiveness of the proposed architecture is underlined via a
large series of experiments conducted on the ScanNet dataset where our approach
compares favorably against both traditional and deep learning techniques.
