Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc.

翻译：如今，摄影测量生成的点云因其低成本和高采集灵活性被广泛应用于民用领域。通常，摄影测量点云通过参考数据（如激光雷达点云）进行评估。然而，当参考数据不可用时，摄影测量点云的评估可能面临挑战。由于这些点云通过算法生成，其精度和准确度随相机网络、场景复杂度和密集影像匹配算法的变化而显著不同，且缺乏标准误差指标来判定逐点误差。相机网络内部可靠性理论已通过光束法平差的一阶误差估计得到深入研究，该理论用于在已知测量误差的前提下理解三维点的误差。然而，密集影像匹配算法的测量误差极其复杂，以至于每个点可能由像素强度、纹理熵和表面平滑度等因素决定其误差函数。尽管复杂度较高，但仍存在一些通用度量指标，有助于评估生成点的后验可靠性，尤其在存在冗余的多视角立体配置中。本文利用包含激光雷达参考数据的航空倾斜摄影测量块，在通用多视角立体框架内分析了几种内部匹配度量指标，包括射线汇聚统计、交会角、密集影像匹配能量等。