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Fully convolutional multi‐scale dense networks for monocular depth estimation

Monocular depth estimation is of vital importance in understanding the 3D geometry of a scene. However, inferring the underlying depth is ill‐posed and inherently ambiguous. In this study, two improvements to existing approaches are proposed. One is about a clean improved network architecture, for w...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Jiwei Liu, Yunzhou Zhang, Jiahua Cui, Yonghui Feng, Linzhuo Pang
Format: Artigo
Sprache:Inglês
Veröffentlicht: Wiley 2019-08-01
Schriftenreihe:IET Computer Vision
Schlagworte:
clean improved network architecture
end-to-end fully convolutional multiscale dense networks
dense upsampling blocks
selected skip connection
edge-preserving loss functions
reverse Huber loss
Online-Zugang:https://doi.org/10.1049/iet-cvi.2018.5645
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