  Geometric model fitting is a fundamental research topic in computer vision
and it aims to fit and segment multiple-structure data. In this paper, we
propose a novel superpixel-guided two-view geometric model fitting method
(called SDF), which can obtain reliable and consistent results for real images.
Specifically, SDF includes three main parts: a deterministic sampling
algorithm, a model hypothesis updating strategy and a novel model selection
algorithm. The proposed deterministic sampling algorithm generates a set of
initial model hypotheses according to the prior information of superpixels.
Then the proposed updating strategy further improves the quality of model
hypotheses. After that, by analyzing the properties of the updated model
hypotheses, the proposed model selection algorithm extends the conventional
"fit-and-remove" framework to estimate model instances in multiple-structure
data. The three parts are tightly coupled to boost the performance of SDF in
both speed and accuracy, and SDF has the deterministic nature. Experimental
results show that the proposed SDF has significant advantages over several
state-of-the-art fitting methods when it is applied to real images with
single-structure and multiple-structure data.
