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.
because it could inexpensively complement LIDAR sensors usedinself-drivingcars,andenablenewsingle-photoappli-cations such as image-editing and AR-compositing. Solv-ing for depth is also a powerful way to use large unlabeled image datasets for the pretraining of deep networks for downstream discriminative tasks [23]. However, collecting
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