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.

中文翻译：

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超像素引导的两视图确定性几何模型拟合

几何模型拟合是计算机视觉中的一个基础研究课题，旨在对多结构数据进行拟合和分割。在本文中，我们提出了一种新颖的超像素引导的两视图几何模型拟合方法（称为 SDF），该方法可以为真实图像获得可靠且一致的结果。具体来说，SDF包括三个主要部分：确定性采样算法、模型假设更新策略和新的模型选择算法。所提出的确定性采样算法根据超像素的先验信息生成一组初始模型假设。然后提出的更新策略进一步提高了模型假设的质量。之后，通过分析更新后的模型假设的性质，所提出的模型选择算法扩展了传统的“拟合和删除”框架，以估计多结构数据中的模型实例。这三个部分紧密耦合以提高 SDF 在速度和精度方面的性能，并且 SDF 具有确定性。实验结果表明，当应用于具有单结构和多结构数据的真实图像时，所提出的 SDF 比几种最先进的拟合方法具有显着优势。