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dc.contributor.authorGafni, Niven_US
dc.contributor.authorSharf, Andreien_US
dc.contributor.editorThomas Funkhouser and Shi-Min Huen_US
dc.date.accessioned2015-03-03T12:41:43Z
dc.date.available2015-03-03T12:41:43Z
dc.date.issued2014en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12432en_US
dc.description.abstractCrowded motions refer to multiple objects moving around and interacting such as crowds, pedestrians and etc. We capture crowded scenes using a depth scanner at video frame rates. Thus, our input is a set of depth frames which sample the scene over time. Processing such data is challenging as it is highly unorganized, with large spatiotemporal holes due to many occlusions. As no correspondence is given, locally tracking 3D points across frames is hard due to noise and missing regions. Furthermore global segmentation and motion completion in presence of large occlusions is ambiguous and hard to predict. Our algorithm utilizes Gestalt principles of common fate and good continuity to compute motion tracking and completion respectively. Our technique does not assume any pregiven markers or motion template priors. Our key-idea is to reduce the motion completion problem to a 1D curve fitting and matching problem which can be solved efficiently using a global optimization scheme. We demonstrate our segmentation and completion method on a variety of synthetic and real world crowded scanned scenes.en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.title3D Motion Completion in Crowded Scenesen_US
dc.description.seriesinformationComputer Graphics Forumen_US


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