Superpixel-guided Two-view Deterministic Geometric Model Fitting

TL;DR

This paper introduces SDF, a superpixel-guided deterministic method for two-view geometric model fitting that improves accuracy and speed in segmenting multiple structures in real images.

Contribution

The paper presents a novel deterministic sampling, hypothesis updating, and model selection framework leveraging superpixels for improved geometric model fitting.

Findings

SDF outperforms state-of-the-art methods in accuracy.

SDF is faster and more reliable on real images.

Effective in both single-structure and multiple-structure scenarios.

Abstract

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.