Transcription of Digging Into Self-Supervised Monocular Depth Estimation
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Digging Into Self-Supervised Monocular Depth Estimation
Digging Into Self-Supervised Monocular Depth EstimationCl ement Godard1 Oisin Mac Aodha2 Michael Firman3 Gabriel Brostow3, ground-truth Depth data is challenging to ac-quire at scale. To overcome this limitation, self-supervisedlearning has emerged as a promising alternative for train-ing models to perform Monocular Depth Estimation . In thispaper, we propose a set of improvements, which together re-sult in both quantitatively and qualitatively improved depthmaps compared to competing Self-Supervised on Self-Supervised Monocular training usuallyexplores increasingly complex architectures, loss functions,and image formation models, all of which have recentlyhelped to close the gap with fully-supervised methods. Weshow that a surprisingly simple model, and associated de-sign choices, lead to superior predictions.
the novel view given the relative pose between those two views. Classical binocular and multi-view stereo methods typically address this ambiguity by enforcing smoothness in the depth maps, and by computing photo-consistency on patches when solving for per-pixel depth via global opti-mization e.g. [11].
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