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BigSUR: Large-scale Structured Urban Reconstruction

Kelly, TWA; Mitra, NJ; (2017) BigSUR: Large-scale Structured Urban Reconstruction. In: (Proceedings) Aiggraph Asia. Association for Computing Machinery (In press).

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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 façade 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.

Type: Proceedings paper
Title: BigSUR: Large-scale Structured Urban Reconstruction
Event: Aiggraph Asia
Location: Bangkok, Thailand
Dates: 27 November 2017 - 30 November 2017
DOI: 10.1145/3130800.3130823
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: urban modeling, structure, reconstruction, façade parsing and element classification, procedural modeling
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1574513
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