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In most Mobile Laser Scanning (MLS) applications, filtering is a necessary step. In this paper, a segmentation-based filtering methodis proposed for MLS point cloud, where a segment rather than an individual point is the basic processing unit. Particularly, the MLSpoint cloud in some blocks are clustered into segments by a surface growing algorithm, then the object segments are detected andremoved. A segment-based filtering method is employed to detect the ground segments. Two MLS point cloud datasets are used toevaluate the proposed method. Experiments indicate that, compared with the classic progressive TIN (Triangulated IrregularNetwork) densification algorithm, the proposed method is capable of reducing the omission error, the commission error and totalerror by 3.62%, 7.87% and 5.54% on average, respectively

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

Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud