Abstract
Decomposing 3D building models into architectural elements is an essential step in understanding their 3D structure. Although we focus on landmark buildings, our approach generalizes to arbitrary 3D objects. We formulate the decomposition as a multi-label optimization that identifies individual elements of a landmark. This allows our system to cope with noisy, incomplete, outlier-contaminated 3D point clouds. We detect three types of structural cues, namely dominant mirror symmetries, rotational symmetries, and polylines capturing free-form shapes of the landmark not explained by symmetry. Combining these cues enables modeling the variability present in complex 3D models, and robustly decomposing them into architectural structural elements. Our architectural decomposition facilitates significant 3D model compression and shape-specific modeling.
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