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.
Method:
The process of the shape segmentation is summarized in two steps of the 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 the shape segmentation. Machine learning methods are trend of 3D shape segmentations for identification, analysis and reconstruction of large-scale models.
Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation