Coplanar Repeats by Energy Minimization
This work addresses a domain-specific problem in computer vision for applications like 3D reconstruction or image analysis, but it is incremental as it builds on existing energy minimization techniques.
The paper tackles the problem of detecting and rectifying arbitrarily-arranged coplanar repeated elements in images using an energy minimization approach, resulting in a significant improvement in rectification accuracy compared to baseline methods.
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization. The proposed energy functional combines several features that model how planes with coplanar repeats are projected into images and captures global interactions between different coplanar repeat groups and scene planes. An inference framework based on a recent variant of $α$-expansion is described and fast convergence is demonstrated. We compare the proposed method to two widely-used geometric multi-model fitting methods using a new dataset of annotated images containing multiple scene planes with coplanar repeats in varied arrangements. The evaluation shows a significant improvement in the accuracy of rectifications computed from coplanar repeats detected with the proposed method versus those detected with the baseline methods.