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High quality depth map upsampling for 3D-TOF cameras

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dc.contributor.authorPark, Jaesik-
dc.contributor.authorKim, Hyeongwoo-
dc.contributor.authorYu-Wing, Tai-
dc.contributor.authorBrown, Michael S.-
dc.contributor.authorKweon, Inso-
dc.date.accessioned2024-05-09T04:14:55Z-
dc.date.available2024-05-09T04:14:55Z-
dc.date.created2024-05-08-
dc.date.created2024-05-08-
dc.date.issued2011-
dc.identifier.citationProceedings of the IEEE International Conference on Computer Vision, pp.1623-1630-
dc.identifier.issn1550-5499-
dc.identifier.urihttps://hdl.handle.net/10371/201338-
dc.description.abstractThis paper describes an application framework to perform high quality upsampling on depth maps captured from a low-resolution and noisy 3D time-of-flight (3D-ToF) camera that has been coupled with a high-resolution RGB camera. Our framework is inspired by recent work that uses nonlocal means filtering to regularize depth maps in order to maintain fine detail and structure. Our framework extends this regularization with an additional edge weighting scheme based on several image features based on the additional high-resolution RGB input. Quantitative and qualitative results show that our method outperforms existing approaches for 3D-ToF upsampling. We describe the complete process for this system, including device calibration, scene warping for input alignment, and even how the results can be further processed using simple user markup. © 2011 IEEE.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleHigh quality depth map upsampling for 3D-TOF cameras-
dc.typeArticle-
dc.identifier.doi10.1109/ICCV.2011.6126423-
dc.citation.journaltitleProceedings of the IEEE International Conference on Computer Vision-
dc.identifier.wosid000300061900206-
dc.identifier.scopusid2-s2.0-84863011482-
dc.citation.endpage1630-
dc.citation.startpage1623-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Jaesik-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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