Planar-Based Adaptive Down-Sampling of Point Clouds

Abstract Derived point clouds from laser scanners and image-based dense-matching techniques usually include tremendous number of points. Processing (e.g., segmenting) such huge dataset is time-consuming and might not be necessary. For example, a planar surface just needs few points to be defined. In contrast, linear/cylindrical and rough features require more points for reliable modeling since during the data acquisition process, only a portion of linear/cylindrical features is present in the point cloud. This paper introduces an adaptive down-sampling strategy for removing redundant points from high density planar regions while retaining points in planar areas with sparse points and all the points within linear/cylindrical and rough neighborhoods. To demonstrate the feasibility and performance of the proposed procedure, a comparison of segmentation results using original laser and image-based point clouds as well as the adaptively, uniformly, and point-spacing-based down-sampled point clouds are presented while commenting on the computational efficiency and the segmentation quality.