Guaranteed Outlier Removal for Point Cloud Registration with Correspondences
\'Alvaro Parra Bustos, Tat-Jun Chin

TL;DR
This paper introduces a fast, deterministic preprocessing method called guaranteed outlier removal that reduces outliers in 3D point cloud registration, ensuring the preservation of the optimal solution and improving computational efficiency.
Contribution
The paper presents a novel geometric preprocessing technique that guarantees the removal of only false outliers in point cloud registration, enhancing robustness and efficiency.
Findings
Reduces outlier population significantly
Preserves the globally optimal solution
Speeds up subsequent registration processes
Abstract
An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust techniques to require very high computational cost. In this paper, we present a novel preprocessing method called \emph{guaranteed outlier removal} for point cloud registration. Our method reduces the input to a smaller set, in a way that any rejected correspondence is guaranteed to not exist in the…
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