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- W3202682771 abstract "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 (U-MVS) 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 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents." @default.
- W3202682771 created "2021-10-11" @default.
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- W3202682771 date "2021-10-01" @default.
- W3202682771 modified "2023-10-11" @default.
- W3202682771 title "Digging into Uncertainty in Self-supervised Multi-view Stereo" @default.
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- W3202682771 doi "https://doi.org/10.1109/iccv48922.2021.00602" @default.
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