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Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation

Li, Hongdong

Description

Finding the largest consensus set is one of the key ideas used by the original RANSAC for removing outliers in robust-estimation. However, because of its random and non-deterministic nature, RANSAC does not fulfill the goal of consensus set maximization exactly and optimally. Based on global optimization, this paper presents a new algorithm that solves the problem exactly. We reformulate the problem as a mixed integer programming (MIP), and solve it via a tailored branch-and-bound method, where...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
URI: http://hdl.handle.net/1885/54073
Source: Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)
DOI: 10.1109/ICCV.2009.5459398

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