  Multi-view depth estimation methods typically require the computation of a
multi-view cost-volume, which leads to huge memory consumption and slow
inference. Furthermore, multi-view matching can fail for texture-less surfaces,
reflective surfaces and moving objects. For such failure modes, single-view
depth estimation methods are often more reliable. To this end, we propose
MaGNet, a novel framework for fusing single-view depth probability with
multi-view geometry, to improve the accuracy, robustness and efficiency of
multi-view depth estimation. For each frame, MaGNet estimates a single-view
depth probability distribution, parameterized as a pixel-wise Gaussian. The
distribution estimated for the reference frame is then used to sample per-pixel
depth candidates. Such probabilistic sampling enables the network to achieve
higher accuracy while evaluating fewer depth candidates. We also propose depth
consistency weighting for the multi-view matching score, to ensure that the
multi-view depth is consistent with the single-view predictions. The proposed
method achieves state-of-the-art performance on ScanNet, 7-Scenes and KITTI.
Qualitative evaluation demonstrates that our method is more robust against
challenging artifacts such as texture-less/reflective surfaces and moving
objects. Our code and model weights are available at
https://github.com/baegwangbin/MaGNet.
