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Airborne laser scanning and photogrammetry are two main techniques to obtain3D data representing the object surface. Due to the high cost of laserscanning, we want to explore the potential of using point clouds derived bydense image matching (DIM), as effective alternatives to laser scanning data.We present a framework to evaluate point clouds from dense image matching andderived Digital Surface Models (DSM) based on automatically extracted samplepatches. Dense matching error and noise level are evaluated quantitatively atboth the local level and whole block level. Experiments show that the optimalvertical accuracy achieved by dense matching is as follows: the mean offset tothe reference data is 0.1 Ground Sampling Distance (GSD); the maximum offsetgoes up to 1.0 GSD. When additional oblique images are used in dense matching,the mean deviation, the variation of mean deviation and the level of randomnoise all get improved. We also detect a bias between the point cloud and DSMfrom a single photogrammetric workflow. This framework also allows to revealinhomogeneity in the distribution of the dense matching errors due toover-fitted BBA network. Meanwhile, suggestions are given on thephotogrammetric quality control.

Patch-based Evaluation of Dense Image Matching Quality