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


摘要Depth 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.
著者Tak-Wai Hui, Chen Change Loy, Xiaoou Tang
會議名稱The 14th European Conference on Computer Vision
會議論文集題名Computer Vision – ECCV 2016. Lecture Notes in Computer Science

上次更新時間 2018-18-01 於 08:21