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.
We first review the key ideas behind self-supervised train-ing for monocular depth estimation, and then describe our depth estimation network and joint training loss. 3.1. SelfSupervised Training Self-supervised depth estimation frames the learning problem as one of novel view-synthesis, by training a net-
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