Density-guided Incremental Dominant Instance Exploration for Two-view Geometric Model Fitting.

Abstract

Existing two-view multi-model fitting methods typically follow a two-step manner, i.e., model generation and selection, without considering their interaction. Therefore, in the first step, these methods have to generate a considerable number of instances in order to cover all desired ones, which not only offers no guarantees, but also introduces unnecessary expensive calculations. To address this challenge, this study presents a new algorithm, termed as D2Fitting, that incrementally explores dominant instances. Particularly, rather than viewing model generation and selection as two disjoint parts, D2Fitting fully considers their interaction, and thus performs these two subroutines alternatively under a simple yet effective optimization framework. This design can avoid generating too many redundant instances, thus reducing computational overhead and allowing the proposed D2Fitting being real-time. Meanwhile, we further design a novel density-guided sampler to sample high-quality minimal subsets during the model generation process, so as to fully exploit the spatial distribution of the input data. Also, to mitigate the influence of noise on the subsets sampled by the proposed sampler, a global-residual optimization strategy is investigated for the minimal subset refinement. With all the ingredients mentioned above, the proposed D2Fitting can accurately estimate the number and parameters of geometric models and efficiently segment the input data simultaneously. Extensive experiments on several public datasets demonstrate the significant superiority of D2Fitting over several state-of-the-arts.