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 highfrequency domain training method to not only reduce training time but also facilitate the fusion of depth and intensity features. With the multiscale guidance, MSG-Net achieves state-of-art performance for depth map upsampling.
CITATION STYLE
Hui, T. W., Loy, C. C., & Tang, X. (2016). Depth map super-resolution by deep multi-scale guidance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9907 LNCS, pp. 353–369). Springer Verlag. https://doi.org/10.1007/978-3-319-46487-9_22
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