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This paper focuses on developing efficient and robust evaluation metrics forRANSAC hypotheses to achieve accurate 3D rigid registration. Estimatingsix-degree-of-freedom (6-DoF) pose from feature correspondences remains apopular approach to 3D rigid registration, where random sample consensus(RANSAC) is a de-facto choice to this problem. However, existing metrics forRANSAC hypotheses are either time-consuming or sensitive to common nuisances,parameter variations, and different application scenarios, resulting inperformance deterioration in overall registration accuracy and speed. Wealleviate this problem by first analyzing the contributions of inliers andoutliers, and then proposing several efficient and robust metrics withdifferent designing motivations for RANSAC hypotheses. Comparative experimentson four standard datasets with different nuisances and application scenariosverify that the proposed metrics can significantly improve the registrationperformance and are more robust than several state-of-the-art competitors,making them good gifts to practical applications. This work also draws aninteresting conclusion, i.e., not all inliers are equal while all outliersshould be equal, which may shed new light on this research problem.

On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration