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Extracting Cycle-aware Feature Curve Networks from 3D Models | |
Lu, Zhengda1,2; Guo, Jianwei1,2![]() ![]() ![]() | |
发表期刊 | COMPUTER-AIDED DESIGN
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ISSN | 0010-4485 |
2021-02-01 | |
卷号 | 131页码:11 |
摘要 | Meaningful feature curves provide high-level shape representation of the geometrical shapes and are useful in various applications. In this paper, we propose an automatic method on the basis of the quadric surface fitting technique to extract complete feature curve networks (FCNs) from 3D surface meshes, as well as finding cycles and generating a high-quality segmentation. In the initial collection of noisy and fragmented feature curves, we first fit the quadric surfaces of each curve and the corresponding neighbor vertices to filter out non-salient or noisy feature curves. Then we conduct a feature extension step to address the curve intersections and form a closed FCN. Finally, we regard circle curves as cycles in the complete FCN and segment the mesh into patches to reveal a highly structured representation of the input geometry. Experimental results demonstrate that our algorithm is more robust for FCN extraction from complex input meshes and achieves higher quality patch layouts compared with the state-of-the-art approaches. We also verify the validity of extracted feature curve cycles by applying them to surface reconstruction. (c) 2020 Elsevier Ltd. All rights reserved. |
关键词 | Shape analysis Feature curve network Segmentation |
DOI | 10.1016/j.cad.2020.102949 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program, China[2018YFB2100602] ; NSFC, China[61802406] ; NSFC, China[61772523] ; Beijing science and technology project, China[Z181100003818019] ; Youth Innovation Promotion Association of CAS, China[Y201935] ; Beijing Natural Science Foundation, China[L182059] ; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems Beihang University, China[VRLAB2019B02] ; CCF-Tencent Open Research Fund, China[RAGR20190105] ; Key Research Program of Frontier Sciences CAS, China[QYZDY-SSW-SYS004] |
项目资助者 | National Key R&D Program, China ; NSFC, China ; Beijing science and technology project, China ; Youth Innovation Promotion Association of CAS, China ; Beijing Natural Science Foundation, China ; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems Beihang University, China ; CCF-Tencent Open Research Fund, China ; Key Research Program of Frontier Sciences CAS, China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Software Engineering |
WOS记录号 | WOS:000596601700004 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42815 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Guo, Jianwei; Xiao, Jun |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Lu, Zhengda,Guo, Jianwei,Xiao, Jun,et al. Extracting Cycle-aware Feature Curve Networks from 3D Models[J]. COMPUTER-AIDED DESIGN,2021,131:11. |
APA | Lu, Zhengda,Guo, Jianwei,Xiao, Jun,Wang, Ying,Zhang, Xiaopeng,&Yan, Dong-Ming.(2021).Extracting Cycle-aware Feature Curve Networks from 3D Models.COMPUTER-AIDED DESIGN,131,11. |
MLA | Lu, Zhengda,et al."Extracting Cycle-aware Feature Curve Networks from 3D Models".COMPUTER-AIDED DESIGN 131(2021):11. |
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