Volume XLII-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 339-344, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 339-344, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Feb 2017

23 Feb 2017

A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

E. Grilli, F. Menna, and F. Remondino E. Grilli et al.
  • 3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: Point Clouds, Segmentation, Classification, Photogrammetry, Laser Scanning

Abstract. Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.