Abstract
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.
Here’s a video of the interactive system in action:
BibTeX
@article{kelly2017bigsur,
title = {BigSUR: Large-scale structured urban reconstruction},
author = {Tom Kelly and John Femiani and Peter Wonka and Niloy J Mitra},
doi = {https://dx.doi.org/10.1145/3130800.3130823},
year = {2017},
date = {2017-01-01},
journal = {ACM Transactions on Graphics},
volume = {36},
number = {6},
publisher = {Association for Computing Machinery},
}