We propose three novel metrics for evaluating the accuracy of a set of estimated camera poses given the ground truth: Translation Alignment Score (TAS), Rotation Alignment Score (RAS), and Pose Alignment Score (PAS). The TAS evaluates the translation accuracy independently of the rotations, and the RAS evaluates the rotation accuracy independently of the translations. The PAS is the average of the two scores, evaluating the combined accuracy of both translations and rotations. The TAS is computed in four steps: (1) Find the upper quartile of the closest-pair-distances, $d$. (2) Align the estimated trajectory to the ground truth using a robust registration method. (3) Collect all distance errors and obtain the cumulative frequencies for multiple thresholds ranging from $0.01d$ to $d$ with a resolution $0.01d$. (4) Add up these cumulative frequencies and normalize them such that the theoretical maximum is 1. The TAS has practical advantages over the existing metrics in that (1) it is robust to outliers and collinear motion, and (2) there is no need to adjust parameters on different datasets. The RAS is computed in a similar manner to the TAS and is also shown to be more robust against outliers than the existing rotation metrics. We verify our claims through extensive simulations and provide in-depth discussion of the strengths and weaknesses of the proposed metrics.

翻译：我们提出了三种新颖的指标，用于在给定真实值的情况下评估一组估计相机姿态的精度：平移对齐分数（TAS）、旋转对齐分数（RAS）和姿态对齐分数（PAS）。TAS独立于旋转评估平移精度，RAS独立于平移评估旋转精度。PAS是这两个分数的平均值，用于评估平移和旋转的综合精度。TAS的计算分为四个步骤：（1）找到最近点对距离的上四分位数 $d$。（2）使用鲁棒配准方法将估计轨迹与真实轨迹对齐。（3）收集所有距离误差，并获取从 $0.01d$ 到 $d$、分辨率为 $0.01d$ 的多个阈值下的累积频率。（4）将这些累积频率相加并进行归一化，使得理论最大值为1。与现有指标相比，TAS具有以下实际优势：（1）对异常值和共线运动具有鲁棒性；（2）无需针对不同数据集调整参数。RAS的计算方式与TAS类似，并且也被证明比现有的旋转指标对异常值更具鲁棒性。我们通过大量仿真验证了我们的主张，并对所提出指标的优缺点进行了深入讨论。