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dc.contributor.authorKelly, Tom
dc.contributor.authorFemiani, John
dc.contributor.authorWonka, Peter
dc.contributor.authorMitra, Niloy J.
dc.date.accessioned2018-01-15T06:10:39Z
dc.date.available2018-01-15T06:10:39Z
dc.date.issued2017-11-22
dc.identifier.citationKelly T, Femiani J, Wonka P, Mitra NJ (2017) BigSUR. ACM Transactions on Graphics 36: 1–16. Available: http://dx.doi.org/10.1145/3130800.3130823.
dc.identifier.issn0730-0301
dc.identifier.doi10.1145/3130800.3130823
dc.identifier.urihttp://hdl.handle.net/10754/626748
dc.description.abstractThe creation of high-quality semantically parsed 3D models for dense metropolitan areas is a fundamental urban modeling problem. Although recent advances in acquisition techniques and processing algorithms have resulted in large-scale imagery or 3D polygonal reconstructions, such data-sources are typically noisy, and incomplete, with no semantic structure. In this paper, we present an automatic data fusion technique that produces high-quality structured models of city blocks. From coarse polygonal meshes, street-level imagery, and GIS footprints, we formulate a binary integer program that globally balances sources of error to produce semantically parsed mass models with associated facade elements. We demonstrate our system on four city regions of varying complexity; our examples typically contain densely built urban blocks spanning hundreds of buildings. In our largest example, we produce a structured model of 37 city blocks spanning a total of 1,011 buildings at a scale and quality previously impossible to achieve automatically.
dc.description.sponsorshipWe would like to thank the many people who contributed to this paper; the reviewers, image labellers, and others who read manuscripts, each made valuable contributions. In particular, we thank Florent Lafarge, Pierre Alliez, Pascal Muller, and Lama Affara for providing us with comparisons, software, and sourcecode, as well as Virginia Unkefer, Robin Roussel, Carlo Innamorati, and Aron Monszpart for their feedback. This work was supported by the ERC Starting Grant (SmartGeometry StG-2013-335373), KAUST-UCL grant (OSR-2015-CCF-2533), the KAUST Office of Sponsored Research (award No. OCRF-2014-CGR3-62140401), the Salt River Project Agricultural Improvement and Power District Cooperative Agreement No. 12061288, and the Visual Computing Center (VCC) at KAUST.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3130800.3130823
dc.relation.urlhttps://youtu.be/_lHRTBkC-yo
dc.rightsArchived with thanks to ACM Transactions on Graphics
dc.subjecturban modeling
dc.subjectstructure
dc.subjectreconstruction
dc.subjectfacade parsing and element classification
dc.subjectprocedural modeling
dc.titleBigSUR: large-scale structured urban reconstruction
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalACM Transactions on Graphics
dc.conference.date2017-11-27 to 2017-11-30
dc.conference.nameACM SIGGRAPH Asia Conference, SA 2017
dc.conference.locationBangkok, THA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity College London
dc.contributor.institutionMiami University
dc.relation.embedded<iframe width="560" height="315" src="https://www.youtube.com/embed/_lHRTBkC-yo?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
kaust.personWonka, Peter
kaust.grant.numberOSR-2015-CCF-2533
kaust.grant.numberOCRF-2014-CGR3-62140401
refterms.dateFOA2018-06-13T19:21:43Z


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