In this paper, an approach for the similarity-based global optimization of buildings in urban scene is presented. In the
past, most researches concentrated on single building reconstruction, making it difficult to reconstruct reliable models
from noisy or incomplete point clouds. To obtain a better result, a new trend is to utilize the similarity among the
buildings. Therefore, a new similarity detection and global optimization strategy is adopted to modify local-fitting
geometric errors. Firstly, the hierarchical structure that consists of geometric, topological and semantic features is
constructed to represent complex roof models. Secondly, similar roof models can be detected by combining primitive
structure and connection similarities. At last, the global optimization strategy is applied to preserve the consistency and
precision of similar roof structures. Moreover, non-local consolidation is adapted to detect small roof parts. The
experiments reveal that the proposed method can obtain convincing roof models and promote the reconstruction quality
of 3D buildings in urban scene.
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