Multi-view repetitive structure detection

Symmetry, especially repetitive structures in architecture are universally demonstrated across countries and cultures. Existing detection methods mainly focus on the detection of planar patterns from a single image. It is difficult to apply them to detect repetitive structures in architecture, which abounds with non-planar 3D repetitive elements (such as balconies and windows) and curved surfaces. We study the repetitive structure detection problem from multiple images of such architecture. Our method jointly analyzes these images and a set of 3D points reconstructed from them by structure-from-motion algorithms. 3D points help to rectify geometric deformations and hypothesize possible lattice structures, while images provide denser color and texture information to evaluate and confirm these hypotheses. In the experiments, we compare our method with existing algorithm. We also show how our results might be used to assist image-based modeling.