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Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point\udclouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial\udimages owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter\udalgorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which\udinfluence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM\udpoint clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on\udthe roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the\uddensification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds\udlocated in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the\udmethod proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better\udfilter results compared with TerraSolid.\ud1

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT CLOUDS