论文标题
实时的平稳变形基于场的不匹配去除
Smooth Deformation Field-based Mismatch Removal in Real-time
论文作者
论文摘要
本文研究了不匹配的删除问题，这可能是特征匹配的后续步骤。非刚性变形使其难以删除不匹配，因为找不到参数转换。为了解决这个问题，我们首先提出了一种基于重新加权和1点兰萨克策略（R1P-RNSC）的算法，这是在合理假设下，这是一种参数方法，即可以通过多个局部刚性变换来近似地表示非刚性变形。 R1P-RNSC很快，但缺点是无法考虑本地平滑信息。然后，我们根据期望最大化算法和双重四基因（EMDQ）表示，提出了一种非参数算法，以生成光滑的变形场。这两种算法互相补偿彼此的弊端。具体而言，EMDQ需要R1P-RNSC提供的良好初始值，而R1P-RNSC需要EMDQ进行改进。现实世界数据的实验结果表明，与其他最先进的方法相比，两种算法的组合具有最佳准确性，该方法可以实时处理多达85％的异常值。实时与异常值的稀疏匹配产生密集变形场的能力使所提出的算法具有许多潜在的应用，例如非刚性注册和SLAM。
This paper studies the mismatch removal problem, which may serve as the subsequent step of feature matching. Non-rigid deformation makes it difficult to remove mismatches because no parametric transformation can be found. To solve this problem, we first propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which is a parametric method under a reasonable assumption that the non-rigid deformation can be approximately represented by multiple locally rigid transformations. R1P-RNSC is fast but suffers from a drawback that the local smoothing information cannot be taken into account. Then, we propose a non-parametric algorithm based on the expectation maximization algorithm and dual quaternion (EMDQ) representation to generate the smooth deformation field. The two algorithms compensate for the drawbacks of each other. Specifically, EMDQ needs good initial values provided by R1P-RNSC, and R1P-RNSC needs EMDQ for refinement. Experimental results with real-world data demonstrate that the combination of the two algorithms has the best accuracy compared to other state-of-the-art methods, which can handle up to 85% of outliers in real-time. The ability to generate dense deformation field from sparse matches with outliers in real-time makes the proposed algorithms have many potential applications, such as non-rigid registration and SLAM.
