Pairwise point cloud registration (PCR) is a crucial problem in photogrammetry, which aims to find a rigid transformation that registers a pair of point clouds. Typically, PCR is performed in a coarse-to-fine manner. Coarse registration provides good initial transformations for fine registration, which determines whether the PCR can succeed. RANSAC-based correspondence registration is the most popular technique for coarse registration. However, the outlier rate of feature correspondences extracted from point clouds is generally very high. Current RANSAC-variants require a huge number of trials to achieve satisfactory results at high outlier rates. This paper proposes a fast and robust RANSAC-variant for PCR, called graph enhanced sample consensus (GESAC). GESAC improves classic RANSAC-family in both sampling and model fitting steps. In the sampling, GESAC generates a much larger subset instead of a minimal subset for model fitting. RANSAC-variants treat a subset as a good one only if the correspondences in the subset are all inliers. In contrast to RANSAC-variants, GESAC allows outliers in the subset and only requires three inliers in the case of point cloud coregistration. Hence, the probability to obtain good subsets of GESAC is much larger than the ones of classic RANSAC-variants. GESAC uses an equal-length constraint to filter “degraded” subsets and expresses a subset as a graph. Then, a max-pooling graph matching strategy is applied to remove potential outliers in the subset. In the model fitting, GESAC introduces a shape-annealing robust estimate instead of classic least-squares for rigid transformation estimation. Hence, even if the subset cleaned by graph matching still contains outliers, GESAC is able to recover a correct solution for PCR. Both simulated and real experiments demonstrate the power of GESAC, i.e., it can tolerate up to more than 99% outliers and is 4000 + times faster than RANSAC at outlier rates above 99% (Note that the running time of feature extraction is not included).

成对的点云配准（PCR）是摄影测量学中的关键问题，其目的是找到能记录一对点云的刚性变换。通常，PCR以从粗到细的方式进行。粗注册为精细注册提供了良好的初始转换，这决定了PCR是否可以成功。基于RANSAC的对应注册是最流行的粗注册技术。但是，从点云中提取的特征对应关系离群率通常很高。当前的RANSAC变体需要大量试验才能以较高的异常值率获得满意的结果。本文提出了一种用于PCR的快速且强大的RANSAC变量，称为图形增强样本共有（GESAC）。GESAC在采样和模型拟合步骤上都改进了经典的RANSAC系列。在抽样中，GESAC会生成更大的子集，而不是用于模型拟合的最小子集。仅当子集中的对应关系全部为内联词时，RANSAC变量才将其视为好子集。与RANSAC变量相比，GESAC允许子集中的异常值，并且在点云合并的情况下仅需要三个异常值。因此，获得GESAC好的子集的可能性比经典的RANSAC变量大得多。GESAC使用等长约束来过滤“降级”的子集并将子集表示为图形。然后，应用最大池图匹配策略以除去子集中的潜在异常值。在模型拟合中，GESAC引入了形状退火鲁棒估计，而不是经典的最小二乘法来进行刚性变换估计。因此，即使通过图匹配清除的子集仍然包含异常值，GESAC仍能够为PCR恢复正确的溶液。模拟实验和真实实验都证明了GESAC的强大功能，即在99％以上的异常率下，它可以忍受超过99％的异常值，并且比RANSAC快4000倍以上（请注意，不包括特征提取的运行时间） 。