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Nowadays, photogrammetrically derived point clouds are widely used in manycivilian applications due to their low cost and flexibility in acquisition.Typically, photogrammetric point clouds are assessed through reference datasuch as LiDAR point clouds. However, when reference data are not available, theassessment of photogrammetric point clouds may be challenging. Since thesepoint clouds are algorithmically derived, their accuracies and precisions arehighly varying with the camera networks, scene complexity, and dense imagematching (DIM) algorithms, and there is no standard error metric to determineper-point errors. The theory of internal reliability of camera networks hasbeen well studied through first-order error estimation of Bundle Adjustment(BA), which is used to understand the errors of 3D points assuming knownmeasurement errors. However, the measurement errors of the DIM algorithms areintricate to an extent that every single point may have its error functiondetermined by factors such as pixel intensity, texture entropy, and surfacesmoothness. Despite the complexity, there exist a few common metrics that mayaid the process of estimating the posterior reliability of the derived points,especially in a multi-view stereo (MVS) setup when redundancies are present. Inthis paper, by using an aerial oblique photogrammetric block with LiDARreference data, we analyze several internal matching metrics within a commonMVS framework, including statistics in ray convergence, intersection angles,DIM energy, etc.

A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo