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An efficient and robust algorithm for 3D mesh segmentation
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AI
DOI：10.1007/s11042-006-0002-x.png)
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Abstract
En 中文
This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.
Keywords:
3D mesh
Gaussian curvature
concaveness
XMR neighborhood
watershed algorithm
region merging
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