Re-weighting and 1-Point RANSAC-Based PnP Solution to Handle Outliers
This work addresses a domain-specific problem for computer vision practitioners by providing an incremental improvement in outlier handling for PnP algorithms.
The paper tackles the problem of handling outliers in perspective-n-point (PnP) solutions, which is crucial for practical applications, by proposing R1PPnP, a fast method that uses soft re-weighting and 1-point RANSAC. Experiments show it is faster than RANSAC+P3P/P4P methods, especially with high outlier percentages, and is accurate, though slower than REPPnP but without its outlier percentage limitation.
The ability to handle outliers is essential for performing the perspective-n-point (PnP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast PnP solution named R1PPnP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a PnP algorithm, which serves as the core of R1PPnP, for solving the PnP problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PPnP is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PPnP is among the most accurate and fast PnP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPPnP, which is the state-of-the-art PnP algorithm with an explicit outliers-handling mechanism, R1PPnP is slower but does not suffer from the percentage of outliers limitation as REPPnP.