BigSUR: large-scale structured urban reconstruction

Tom Kelly, John Femiani, Peter Wonka, Niloy J. Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations


The 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.
Original languageEnglish (US)
Title of host publicationACM Transactions on Graphics
PublisherAssociation for Computing Machinery (ACM)
Number of pages16
StatePublished - Nov 22 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CCF-2533, OCRF-2014-CGR3-62140401
Acknowledgements: We 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.


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