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S. Ikehata, J. Cho, K. Aizawa:
"Depth Map Inpainting and Super-Resolution based on Internal Statistics of Geometry and Appearance";
Poster: IEEE International Conference on Image Processing, Melbourne, Australia; 2013-09-15 - 2013-09-18; in: "Proc. of ICIP", IEEE, (2013), 938 - 942.

Depth maps captured by multiple sensors often suffer from
poor resolution and missing pixels caused by low reflectivity
and occlusions in the scene. To address these problems,
we propose a combined framework of patch-based inpainting
and super-resolution. Unlike previous works, which relied
solely on depth information, we explicitly take advantage of
the internal statistics of a depth map and a registered highresolution
texture image that capture the same scene. We
account these statistics to locate non-local patches for hole
filling and constrain the sparse coding-based super-resolution
problem. Extensive evaluations are performed and show the
state-of-the-art performance when using real-world datasets.

depth-map super-resolution, depth-map inpainting, ToF sensor, sparse Bayesian learning

Project Head Margrit Gelautz:
3D VideoFusion