Self-supervised Multi-view stereo (MVS) with a pretext task of image
reconstruction has achieved significant progress recently. However, previous
methods are built upon intuitions, lacking comprehensive explanations about the
effectiveness of the pretext task in self-supervised MVS. To this end, we
propose to estimate epistemic uncertainty in self-supervised MVS, accounting
for what the model ignores. Specially, the limitations can be categorized into
two types: ambiguious supervision in foreground and invalid supervision in
background. To address these issues, we propose a novel Uncertainty reduction
Multi-view Stereo (UMVS) framework for self-supervised learning. To alleviate
ambiguous supervision in foreground, we involve extra correspondence prior with
a flow-depth consistency loss. The dense 2D correspondence of optical flows is
used to regularize the 3D stereo correspondence in MVS. To handle the invalid
supervision in background, we use Monte-Carlo Dropout to acquire the
uncertainty map and further filter the unreliable supervision signals on
invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show
that our U-MVS framework achieves the best performance among unsupervised MVS
methods, with competitive performance with its supervised opponents.

提出了一种新的 UMVS 框架，通过估计自监督 MVS 中的认知不确定性来处理前景中的不确定监督和背景中的无效监督信号，并在 DTU 和 Tank＆Temples 基准测试中获得了最佳性能。