The present Multi-view stereo (MVS) methods with supervised learning-based
networks have an impressive performance comparing with traditional MVS methods.
However, the ground-truth depth maps for training are hard to be obtained and
are within limited kinds of scenarios. In this paper, we propose a novel
unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud
reconstruction without any supervision. To improve the robustness and
completeness of point cloud reconstruction, we propose a novel multi-metric
loss function that combines pixel-wise and feature-wise loss function to learn
the inherent constraints from different perspectives of matching
correspondences. Besides, we also incorporate the normal-depth consistency in
the 3D point cloud format to improve the accuracy and continuity of the
estimated depth maps. Experimental results show that M3VSNet establishes the
state-of-the-arts unsupervised method and achieves comparable performance with
previous supervised MVSNet on the DTU dataset and demonstrates the powerful
generalization ability on the Tanks and Temples benchmark with effective
improvement. Our code is available at this https URL

本文提出了一种名为 M^3VSNet 的新型无监督多指标 MVS 网络，用于稠密点云重建，该网络通过结合像素和特征损失函数来学习来自不同视角的匹配对应的内在约束，并将深度图从相机空间转化为点云，并改善了深度图的精准性和连续性，该方法在 DTU 数据集上表现出了与以往有监督方法可比较的性能，并在坦克和寺庙基准测试中证明了其强大的泛化能力。