A REVIEW OFPOINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

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.

• Publication year

  2017

• Venue

  ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2017-02-23

• Fields of study

  Geography, Computer Science, Engineering

• Identifiers
  DOI 10.5194/ISPRS-ARCHIVES-XLII-2-W3-339-2017
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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