Efficient architectural structural element decomposition.

A model that combines symmetries and free-form polylines for decomposing a point cloud into ASEs.Methods for detecting structural cues (dominant mirror symmetries and rotational symmetries, as well as residual free-form parts) in point clouds.A global energy formulation and optimization approach for partitioning a point cloud into meaningful structural components based on structural cues. 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 four types of structural cues, namely dominant mirror symmetries, rotational symmetries, shape primitives, and polylines capturing free-form shapes of the landmark not explained by symmetry. Our novel method combine these cues enables modeling the variability present in complex 3D models, and robustly decomposing them into architectural structural elements. Our proposed architectural decomposition facilitates significant 3D model compression and shape-specific modeling.