Deformable object model matching by topological and geometric similarity
In this paper, we present a novel method for efficient 3D model comparison. The method is designed to match highly deformed models through capturing two types of information. First, we propose a feature point extraction algorithm, which is based on “Level Set Diagram”, to reliably capture the topolo...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2004
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/6608 https://ink.library.smu.edu.sg/context/sis_research/article/7611/viewcontent/21710335.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this paper, we present a novel method for efficient 3D model comparison. The method is designed to match highly deformed models through capturing two types of information. First, we propose a feature point extraction algorithm, which is based on “Level Set Diagram”, to reliably capture the topological points of a general 3D model. These topological points represent the skeletal structure of the model. Second, we also capture both spatial and curvature information, which describes the global surface of a 3D model. This is different from traditional topological 3D matching methods that use only low-dimension local features. Our method can accurately distinguish different types of 3D models even if they have similar topology. By applying the bipartite graph matching technique, our method can achieve a high precision of 0.54 even at a recall rate of 1.0 as demonstrated in our experimental results. |
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