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Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Review Article

3D Shape Segmentation: A Review

Author(s): Rui Li and Qingjin Peng*

Volume 16, Issue 5, 2022

Published on: 03 February, 2021

Article ID: e210422191089 Pages: 17

DOI: 10.2174/1872212115666210203152106

Price: $65

Abstract

Background: Shape segmentation is commonly required in many engineering fields to separate a 3D shape into pieces for some specific applications. Although there are different methods proposed to segment the 3D shape, there is a lack of analyses of their efficiency and accuracy. It is a challenge to select an effective method to meet a particular requirement of the shape segmentation.

Objective: This paper reviews existing methods of the shape segmentation to summarize the methods and processes to identify their pros and cons.

Methods: The process of the shape segmentation is summarized in two steps: feature extraction and model separation.

Results: Shape features are identified from the available methods. Different methods of the shape segmentation are evaluated. The challenge and trend of the shape segmentation are discussed.

Conclusion: Clustering is the most used method for shape segmentation. Machine learning methods are a trend of 3D shape segmentations for identification, analysis and reconstruction of large-scale models.

Keywords: 3D modeling, shape segmentation, shape analysis, feature extraction, mesh model, point clouds.

Graphical Abstract

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