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.

本文介绍了一种利用深度神经网络复制传统的 local depth maps calculation 和 global depth maps fusion 两步骤框架，以改善对 3D 场景重建精度和可解释性的计算机视觉任务方法。此外，作者还提出了一种称为 PosedConv 的旋转不变的 3D 卷积核，用于提高从非常不同视角获取的图像之间的匹配效率。作者在 ScanNet 数据集上进行了大量实验证明提出的方法在深度神经网络和传统计算机视觉技术中具有竞争力。