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