SENSE : Self-Evolving Learning for Self-Supervised Monocular Depth Estimation

Self-supervised depth estimation methods can achieve competitive performance using only unlabeled monocular videos, but they suffer from the uncertainty of jointly learning depth and pose without any ground truths of both tasks. Supervised framework provides robust and superior performance but is li...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 33(2024) vom: 25., Seite 439-450
1. Verfasser:	Li, Guanbin (VerfasserIn)
Weitere Verfasser:	Huang, Ricong, Li, Haofeng, You, Zunzhi, Chen, Weikai
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2024
Zugriff auf das übergeordnete Werk:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:	Journal Article