  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.
