Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data

TL;DR

This paper introduces a superpixel-based deterministic fitting method that efficiently estimates multiple geometric models in data with multiple structures, improving speed and accuracy over existing methods.

Contribution

The paper presents a novel superpixel-based deterministic sampling and model selection approach that enhances multi-structure data fitting performance.

Findings

Outperforms state-of-the-art methods in speed and accuracy.

Effectively handles multi-structure data in real images.

Reduces computational complexity through superpixel segmentation.

Abstract

This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fit-and-remove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with single-structure and multiple-structure data.