论文标题
重新加权和基于1分RANSAC的PNP解决方案来处理异常值
Re-weighting and 1-Point RANSAC-Based PnP Solution to Handle Outliers
论文作者
论文摘要
处理异常值的能力对于在实际应用中执行透视N点（PNP）方法至关重要，但是常规的RANSAC+P3P或P4P方法具有很高的时间复杂性。我们提出了一种名为R1PPNP的快速PNP解决方案，以利用软加权机构和1分ransac方案来处理离群值。我们首先提出了一种作为R1PPNP的核心的PNP算法，用于解决无离群的情况下的PNP问题。核心算法是一个最佳过程，可以最大程度地减少使用随机控制点进行的目标函数。然后，为了减少异常值的影响，我们提出了一种基于再投入错误的重新加权方法，并将其集成到核心算法中。最后，我们采用1分RANSAC方案来尝试不同的控制点。合成和现实世界数据的实验表明，R1PPNP比RANSAC+P3P或P4P方法快，尤其是当异常值的百分比很大并且准确时。此外，与无离群的合成数据的比较表明，R1PPNP是最准确，最快的PNP解决方案之一，通常是RANSAC+P3P或P4P的最终细化步骤。与具有显式离群机制的最先进的PNP算法相比，R1PPNP较慢，但并未受到REPPNP的离群限制的百分比。
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.
