On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
This work addresses a critical bottleneck in 3D rigid registration for applications like robotics and computer vision, though it is incremental as it builds on existing RANSAC frameworks.
The paper tackles the problem of inefficient and non-robust metrics for RANSAC hypotheses in 3D rigid registration, proposing new metrics that significantly improve registration performance and robustness compared to state-of-the-art methods, as verified on four standard datasets.
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.