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.