Abstract
Abstract. Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.
Highlights
With the development of new pixel-wise matching algorithms and the improvement in camera technology, the dense, reliable and accurate 3D DIM point clouds have been generated by research groups and commercial vendors and have been used in many fields, such as land cover classification(Rau et al, 2015), 3D-building modelling(Dahlke et al, 2015; Maltezos and Ioannidis, 2015) and digital terrain modelling(Li and Gruen, 2004)
It has been shown that the ground points are extracted by Terrasolid, a large number of terrain points are omitted in filtering and are regarded as the objects points from the Figure 7(b’)
It is obvious that the number of type I errors produced by the proposed method is less than Terrasolid because of the application of new densification strategy
Summary
With the development of new pixel-wise matching algorithms and the improvement in camera technology, the dense, reliable and accurate 3D DIM point clouds have been generated by research groups and commercial vendors and have been used in many fields, such as land cover classification(Rau et al, 2015), 3D-building modelling(Dahlke et al, 2015; Maltezos and Ioannidis, 2015) and digital terrain modelling(Li and Gruen, 2004) In these applications, point clouds filtering is still an essential step. Some works(Rau et al, 2015; Themistocleous et al, 2016) have pointed that the differences contain the point density, variation Besides these discrepancies above, the DIM point clouds don’t have scan line, echo times and intensity information either compared with Lidar point clouds. Whether these differences will affect the filter results has not been discussed in previous works
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