Patch based synthesis for single depth image super-resolution

Oisin Mac Aodha, Neill D. F. Campbell, Arun Nair, Gabriel J. Brostow

Research output: Chapter or section in a book/report/conference proceedingChapter or section

170 Citations (SciVal)


We present an algorithm to synthetically increase the resolution of a solitary depth image using only a generic database of local patches. Modern range sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images. We match against the height field of each low resolution input depth patch, and search our database for a list of appropriate high resolution candidate patches. Selecting the right candidate at each location in the depth image is then posed as a Markov random field labeling problem. Our experiments also show how important further depth-specific processing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only synthetic training depth data.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2012
Subtitle of host publicationProceedings of 12th European Conference on Computer Vision, Part III
EditorsA. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, C. Schmid
Place of PublicationBerlin, Germany
Number of pages14
ISBN (Print)9783642337116
Publication statusPublished - 2012
Event12th European Conference on Computer Vision,2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012

Publication series

NameLecture Notes in Computer Science


Conference12th European Conference on Computer Vision,2012


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