Depth Map Super Resolution by Deep Multi-Scale Guidance
Refereed conference paper presented and published in conference proceedings


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AbstractDepth boundaries often lose sharpness when upsampling from
low-resolution (LR) depth maps especially at large upscaling factors. We
present a new method to address the problem of depth map super resolution
in which a high-resolution (HR) depth map is inferred from a
LR depth map and an additional HR intensity image of the same scene.
We propose a Multi-Scale Guided convolutional network (MSG-Net) for
depth map super resolution. MSG-Net complements LR depth features
with HR intensity features using a multi-scale fusion strategy. Such a
multi-scale guidance allows the network to better adapt for upsampling
of both fine- and large-scale structures. Specifically, the rich hierarchical
HR intensity features at different levels progressively resolve ambiguity
in depth map upsampling. Moreover, we employ a high-frequency domain
training method to not only reduce training time but also facilitate
the fusion of depth and intensity features. With the multi-scale guidance,
MSG-Net achieves state-of-art performance for depth map upsampling.
All Author(s) ListTak-Wai Hui, Chen Change Loy, Xiaoou Tang
Name of ConferenceThe 14th European Conference on Computer Vision
Start Date of Conference08/10/2016
End Date of Conference16/10/2016
Place of ConferenceAmsterdam
Country/Region of ConferenceNetherlands
Proceedings TitleComputer Vision – ECCV 2016. Lecture Notes in Computer Science
Year2016
PublisherSpringer
ISBN978-3-319-46486-2
LanguagesEnglish-United States

Last updated on 2018-18-01 at 08:21