2.5D building modeling by discovering global regularities

Authors:
Qian-Yi Zhou
Affiliations:
University of Southern California
Venue:
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year:
2012

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Abstract

We introduce global regularities in the 2.5D building modeling problem, to reflect the orientation and placement similarities between planar elements in building structures. Given a 2.5D point cloud scan, we present an automatic approach that simultaneously detects locally fitted plane primitives and global regularities. While global regularities are extracted by analyzing the plane primitives, they adjust the planes in return and effectively correct local fitting errors. We explore a broad variety of global regularities between 2.5D planar elements including both planer roof patches and planar facade patches. By aligning planar elements to global regularities, our method significantly improves the model quality in terms of both geometry and human judgement.