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Digging into Uncertainty in Self-supervised Multi-view Stereo
[article]
2021
arXiv
pre-print
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
arXiv:2108.12966v2
fatcat:bzcpxvbzazh37n37ce6o4hokne