3D mesh model segmentation based on skeleton extraction. (19th May 2021) - Han, Huiyan; Han, Xie; Gao, Tianyi
ABSTRACT: In order to solve the problem of low segmentation accuracy of 3D mesh models, a non-deep learning method based on skeleton extraction is proposed. First, the skeleton of the model is extracted. Then, the skeleton feature points are extracted by analysing the relationship between the skeleton data. The feature point with the smallest distance from the centroid in the same neighbourhood is selected as the segmentation point. Finally, based on the improved region-based algorithm, the segmented skeleton region is obtained with the segmentation points as the seeds, and the corresponding original data is segmented through inverse mapping. The experimental results show that compared with the existing non-deep learning methods, the proposed method can obtain better semantic separation results and segmentation boundaries.