Unsupervised Learning for Depth, Ego-Motion, and Optical Flow Estimation Using Coupled Consistency Conditions

Herein, we propose an unsupervised learning architecture under coupled consistency conditions to estimate the depth, ego-motion, and optical flow. Previously invented learning techniques in computer vision adopted a large amount of the ground truth dataset for network training. A ground truth datase...

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Detalles Bibliográficos
Autores principales: Mun, Ji-Hun, Jeon, Moongu, Lee, Byung-Geun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603746/
https://www.ncbi.nlm.nih.gov/pubmed/31146404
http://dx.doi.org/10.3390/s19112459

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603746/
https://www.ncbi.nlm.nih.gov/pubmed/31146404
http://dx.doi.org/10.3390/s19112459

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