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dc.contributor.authorAlcantarilla, Pablo F.
dc.contributor.authorBeall, Chris
dc.contributor.authorDellaert, Frank
dc.date.accessioned2014-03-21T20:19:49Z
dc.date.available2014-03-21T20:19:49Z
dc.date.issued2013-11
dc.identifier.citationAlcantarilla, P.F., Beall, C. & Dellaert, F. (2013). Large-Scale Dense 3D Reconstruction from Stereo Imagery. 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles (PPNIV13), November 2013.en_US
dc.identifier.urihttp://hdl.handle.net/1853/51471
dc.description©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.description.abstractIn this paper we propose a novel method for large-scale dense 3D reconstruction from stereo imagery. Assuming that stereo camera calibration and camera motion are known, our method is able to reconstruct accurately dense 3D models of urban environments in the form of point clouds. We take advantage of recent stereo matching techniques that are able to build dense and accurate disparity maps from two rectified images. Then, we fuse the information from multiple disparity maps into a global model by using an efficient data association technique that takes into account stereo uncertainty and performs geometric and photometric consistency validation in a multi-view setup. Finally, we use efficient voxel grid filtering techniques to deal with storage requirements in large-scale environments. In addition, our method automatically discards possible moving obstacles in the scene. We show experimental results on real video large-scale sequences and compare our approach with respect to other state-of-the-art methods such as PMVS and StereoScan.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectComputational complexityen_US
dc.subjectDisparity mapsen_US
dc.subjectEgomotionen_US
dc.subjectImage planeen_US
dc.subjectObstaclesen_US
dc.subjectPoint cloudsen_US
dc.subjectPMVSen_US
dc.subjectRoboticsen_US
dc.subjectStereo imageryen_US
dc.subjectStereoScanen_US
dc.subject3D reconstructionen_US
dc.subjectVoxel grid filteringen_US
dc.titleLarge-Scale Dense 3D Reconstruction from Stereo Imageryen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.embargo.termsnullen_US


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