Three-dimensional Model Segmentation Based on Improved Random Walk Segmentation Algorithm
Authors: 
Wen Yang, Xiaoning Liu, Jiabei Hu, Fei Zhu, Kang Li, Guohua GengAuthors Info & Claims
Article No.: 5, Pages 1 - 5
Abstract
In1 order to solve the problem of the poor precision and instability of the existing 3D model segmentation algorithm, an improved random walk algorithm is proposed for 3D model segmentation. First, an empowerment model is constructed for the input 3D model, and the transformation matrix is obtained. Then, the model is marked as seeds by interaction and divided into k meaningful regions, obtaining the k initial distributions. Finally, the final probability distribution is obtained by iterating to convergence, and the maximum posterior probability is used to the model punctuation, so as to realize the 3D models segmentation. Experiments show that the method has achieved good segmentation results on irregular face models and the Terracotta Army fragment model. Compared with other methods, the algorithm has better performance, fewer iterations and shorter running time.
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Index Terms
- Three-dimensional Model Segmentation Based on Improved Random Walk Segmentation Algorithm
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October 2018
1083 pages
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Association for Computing Machinery
New York, NY, United States
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Published: 22 October 2018
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- Research-article
- Research
- Refereed limited
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- Graduate Scientific Research Foundation of Northwest University
- National Natural Science Foundation of China
- Shaanxi Provincial Natural Science Basic Research Project
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CSAE '18
CSAE '18: The 2nd International Conference on Computer Science and Application Engineering
October 22 - 24, 2018
Hohhot, China
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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;
Overall Acceptance Rate 368 of 770 submissions, 48%
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