Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage

Luca Magri, Andrea Fusiello; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7460-7468

This paper addresses the problem of multiple models fitting in the general context where the sought structures can be described by a mixture of heterogeneous parametric models drawn from different classes. To this end, we conceive a multi-model selection framework that extend T-linkage to cope with different nested class of models. Our method, called MCT, compares favourably with the state-of-the-art on publicly available data-sets for various fitting problems: lines and conics, homographies and fundamental matrices, planes and cylinders.