期刊
ACM TRANSACTIONS ON GRAPHICS
卷 36, 期 6, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3130800.3130823
关键词
urban modeling; structure; reconstruction; facade parsing and element classification; procedural modeling
资金
- ERC Starting Grant [SmartGeometry StG-2013-335373]
- KAUST-UCL grant [OSR-2015-CCF-2533]
- KAUST Office of Sponsored Research [OCRF-2014-CGR3-62140401]
- Salt River Project Agricultural Improvement and Power District [12061288]
- Visual Computing Center (VCC) at KAUST
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.
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