Fully convolutional multi‐scale dense networks for monocular depth estimation

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...

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Bibliographic Details
Main Authors: Jiwei Liu, Yunzhou Zhang, Jiahua Cui, Yonghui Feng, Linzhuo Pang
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:IET Computer Vision
Subjects:
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 Access:https://doi.org/10.1049/iet-cvi.2018.5645

Internet

https://doi.org/10.1049/iet-cvi.2018.5645

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