Deterministic Fitting of Multiple Structures using Iterative MaxFS with Inlier Scale Estimation and Subset Updating

TL;DR

This paper introduces IMaxFS-ISE-SU, a deterministic algorithm for robust multiple structure fitting that iteratively estimates models and inlier scales, reducing data complexity for improved efficiency and reliability in high-outlier scenarios.

Contribution

The paper proposes a novel deterministic method combining iterative MaxFS with inlier scale estimation and subset updating, enhancing robustness and efficiency over traditional random sampling approaches.

Findings

Generates more reliable hypotheses than random sampling methods.

Effective in high-outlier ratio scenarios.

Demonstrates improved accuracy and consistency in multiple structure estimation.

Abstract

We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented MaxFS-based algorithm iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. Further it generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. Our method, called iterative MaxFS with inlier scale estimation and subset updating (IMaxFS-ISE-SU) in this paper, performs hypothesis generation and fitting alternately until all of true structures are found. The IMaxFS-ISE-SU algorithm generates substantially more reliable hypotheses than random sampling-based methods especially as (pseudo-)outlier ratios increase. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers.