RobustMVS: Single Domain Generalized Deep Multi-view Stereo
It addresses performance degradation in MVS when generalizing to unseen domains, which is a domain-specific problem for computer vision applications, but is incremental as it builds on existing MVS methods.
The paper tackles the problem of domain generalization in Multi-view Stereo (MVS) by proposing RobustMVS, which uses a DepthClustering-guided Whitening loss to maintain feature consistency across views, achieving superior performance on a new benchmark with synthetic and real-world datasets.
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.