Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple Constraints

This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models. Unlike the existing unsupervised methods, our method extra...

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Detalles Bibliográficos
Autores principales: Zhao, Baigan, Huang, Yingping, Ci, Wenyan, Hu, Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963015/
https://www.ncbi.nlm.nih.gov/pubmed/35214285
http://dx.doi.org/10.3390/s22041383

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963015/
https://www.ncbi.nlm.nih.gov/pubmed/35214285
http://dx.doi.org/10.3390/s22041383

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