  Despite the impressive performance of Multi-view Stereo (MVS) approaches
given plenty of training samples, the performance degradation when generalizing
to unseen domains has not been clearly explored yet. In this work, we focus on
the domain generalization problem in MVS. To evaluate the generalization
results, we build a novel MVS domain generalization benchmark including
synthetic and real-world datasets. In contrast to conventional domain
generalization benchmarks, we consider a more realistic but challenging
scenario, where only one source domain is available for training. The MVS
problem can be analogized back to the feature matching task, and maintaining
robust feature consistency among views is an important factor for improving
generalization performance. To address the domain generalization problem in
MVS, we propose a novel MVS framework, namely RobustMVS. A
DepthClustering-guided Whitening (DCW) loss is further introduced to preserve
the feature consistency among different views, which decorrelates multi-view
features from viewpoint-specific style information based on geometric priors
from depth maps. The experimental results further show that our method achieves
superior performance on the domain generalization benchmark.
