离散莫尔斯理论的临界点

Peihong Guo, E. Akleman, Ying He, Xiaoning Wang, W. Liu
{"title":"离散莫尔斯理论的临界点","authors":"Peihong Guo, E. Akleman, Ying He, Xiaoning Wang, W. Liu","doi":"10.1145/2787626.2792614","DOIUrl":null,"url":null,"abstract":"In this work, we present some of the unexpected observations resulted from our recent research. We, recently, needed to identify a small number of important critical points, i.e. minimum, maximum and saddle points, on a given manifold mesh surface. All critical points on a manifold triangular mesh can be identified using discrete Gaussian curvature, which is given as ki = 2π − Σj θi,j where ki is vertex defect (the discrete Gaussian curvature) of the vertex i and θi,j is the corner of the vertex in the triangle j. A very useful property coming with vertex defect is the discrete version of Gauss-Bonnet theorem: the sum of all vertex defects is always constant as Σi ki = 2π(2−2g) where g is the genus of the mesh. Any vertex with a non-zero vertex defect is really an critical point of the surface. However, identification of interesting critical points is hard with vertex defect alone. As it can be seen in Figure 1(a), even we ignore vertex defects that are small, too many vertices are still chosen and this information is not really useful to make any conclusion of the shape of the surface.","PeriodicalId":269034,"journal":{"name":"ACM SIGGRAPH 2015 Posters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Critical points with discrete Morse theory\",\"authors\":\"Peihong Guo, E. Akleman, Ying He, Xiaoning Wang, W. Liu\",\"doi\":\"10.1145/2787626.2792614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we present some of the unexpected observations resulted from our recent research. We, recently, needed to identify a small number of important critical points, i.e. minimum, maximum and saddle points, on a given manifold mesh surface. All critical points on a manifold triangular mesh can be identified using discrete Gaussian curvature, which is given as ki = 2π − Σj θi,j where ki is vertex defect (the discrete Gaussian curvature) of the vertex i and θi,j is the corner of the vertex in the triangle j. A very useful property coming with vertex defect is the discrete version of Gauss-Bonnet theorem: the sum of all vertex defects is always constant as Σi ki = 2π(2−2g) where g is the genus of the mesh. Any vertex with a non-zero vertex defect is really an critical point of the surface. However, identification of interesting critical points is hard with vertex defect alone. As it can be seen in Figure 1(a), even we ignore vertex defects that are small, too many vertices are still chosen and this information is not really useful to make any conclusion of the shape of the surface.\",\"PeriodicalId\":269034,\"journal\":{\"name\":\"ACM SIGGRAPH 2015 Posters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGGRAPH 2015 Posters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2787626.2792614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2015 Posters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2787626.2792614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在这项工作中,我们提出了一些意想不到的观察结果,从我们最近的研究。最近,我们需要在给定的流形网格表面上识别少量重要的临界点,即最小点、最大值和鞍点。所有临界点流形三角网格可以使用离散高斯曲率,确定这是作为ki = 2π−Σjθ,j, ki顶点缺陷(离散高斯曲率)的顶点我和θ,j是三角形顶点的角落j。一个非常有用的属性与顶点的缺陷是离散的版本Gauss-Bonnet定理:顶点缺陷总是不断的总和作为Σ我ki = 2π2−2 g, g网的一个属。任何有非零顶点缺陷的顶点都是曲面的一个临界点。然而,仅用顶点缺陷很难识别出感兴趣的临界点。从图1(a)中可以看出,即使我们忽略了较小的顶点缺陷,仍然选择了太多的顶点,这些信息对于得出曲面形状的任何结论都没有真正的用处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Critical points with discrete Morse theory
In this work, we present some of the unexpected observations resulted from our recent research. We, recently, needed to identify a small number of important critical points, i.e. minimum, maximum and saddle points, on a given manifold mesh surface. All critical points on a manifold triangular mesh can be identified using discrete Gaussian curvature, which is given as ki = 2π − Σj θi,j where ki is vertex defect (the discrete Gaussian curvature) of the vertex i and θi,j is the corner of the vertex in the triangle j. A very useful property coming with vertex defect is the discrete version of Gauss-Bonnet theorem: the sum of all vertex defects is always constant as Σi ki = 2π(2−2g) where g is the genus of the mesh. Any vertex with a non-zero vertex defect is really an critical point of the surface. However, identification of interesting critical points is hard with vertex defect alone. As it can be seen in Figure 1(a), even we ignore vertex defects that are small, too many vertices are still chosen and this information is not really useful to make any conclusion of the shape of the surface.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Dynamic fur on mobile using textured offset surfaces Continuous and automatic registration of live RGBD video streams with partial overlapping views Mobile haptic system design to evoke relaxation through paced breathing Virtual headcam: pan/tilt mirror-based facial performance tracking Burning the medial axis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1