International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 153–157, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-153-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 153–157, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-153-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

RESEARCH ON IMPROVED REGION GROWING POINT CLOUD ALGORITHM

C. L. Kang1,2, F. Wang1,2, M. M. Zong1,2, Y. Cheng1,2, and T. N. Lu1,2 C. L. Kang et al.
  • 1Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006,China
  • 2College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

Keywords: Region Growing, Point Cloud Segmentation, Eigenvalues, Curvature, Point Cloud Filtering, Seed Point

Abstract. The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles. Finally, according to the shape characteristics of the point cloud and the preliminary segmentation results, each threshold is adjusted and determined, and the segmentation result is optimized.The experimental results show that compared with the traditional regional growth segmentation algorithm, this method can not only reduce the total number of segmentation regions, but also segment the point cloud data quickly and effectively, and solve the segmentation result caused by the traditional region growth point cloud segmentation method. Problems such as stability improve the accuracy and stability of point cloud segmentation.