Extracting Cycle-aware Feature Curve Networks from 3D Models
Lu, Zhengda1,2; Guo, Jianwei1,2; Xiao, Jun1; Wang, Ying1; Zhang, Xiaopeng1,2; Yan, Dong-Ming1,2
发表期刊COMPUTER-AIDED DESIGN
ISSN0010-4485
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
DOI10.1016/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
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
2021_CAD_Extracting (5733KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Lu, Zhengda]的文章
[Guo, Jianwei]的文章
[Xiao, Jun]的文章
百度学术
百度学术中相似的文章
[Lu, Zhengda]的文章
[Guo, Jianwei]的文章
[Xiao, Jun]的文章
必应学术
必应学术中相似的文章
[Lu, Zhengda]的文章
[Guo, Jianwei]的文章
[Xiao, Jun]的文章
相关权益政策
暂无数据
收藏/分享
文件名: 2021_CAD_Extracting Cycle-aware Feature Curve Networks from 3D Models.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。