TopoCut

计算机科学 稳健性(进化) 几何处理 拓扑(电路) 算法 计算 可视化 网格生成 多边形网格 数学 数学优化 理论计算机科学 人工智能 有限元法 计算机图形学(图像) 组合数学 热力学 基因 物理 生物化学 化学
作者
Xianzhong Fang,Mathieu Desbrun,Hujun Bao,Jin Huang
出处
期刊:ACM Transactions on Graphics [Association for Computing Machinery]
卷期号:41 (4): 1-15 被引量:6
标识
DOI:10.1145/3528223.3530149
摘要

Given a complex three-dimensional domain delimited by a closed and non-degenerate input triangle mesh without any self-intersection, a common geometry processing task consists in cutting up the domain into cells through a set of planar cuts, creating a "cut-cell mesh", i.e., a volumetric decomposition of the domain amenable to visualization (e.g., exploded views), animation (e.g., virtual surgery), or simulation (finite volume computations). A large number of methods have proposed either efficient or robust solutions, sometimes restricting the cuts to form a regular or adaptive grid for simplicity; yet, none can guarantee both properties, severely limiting their usefulness in practice. At the core of the difficulty is the determination of topological relationships among large numbers of vertices, edges, faces and cells in order to assemble a proper cut-cell mesh: while exact geometric computations provide a robust solution to this issue, their high computational cost has prompted a number of faster solutions based on, e.g., local floating-point angle sorting to significantly accelerate the process --- but losing robustness in doing so. In this paper, we introduce a new approach to planar cutting of 3D domains that substitutes topological inference for numerical ordering through a novel mesh data structure, and revert to exact numerical evaluations only in the few rare cases where it is strictly necessary. We show that our novel concept of topological cuts exploits the inherent structure of cut-cell mesh generation to save computational time while still guaranteeing exactness for, and robustness to, arbitrary cuts and surface geometry. We demonstrate the superiority of our approach over state-of-the-art methods on almost 10,000 meshes with a wide range of geometric and topological complexity. We also provide an open source implementation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ohh完成签到,获得积分10
刚刚
水濑心源发布了新的文献求助10
2秒前
许子健发布了新的文献求助10
4秒前
shuyu完成签到,获得积分10
4秒前
哈哈哈完成签到 ,获得积分10
4秒前
RosyBai发布了新的文献求助10
5秒前
5秒前
佳佳应助落后的盼秋采纳,获得10
6秒前
慧1111111发布了新的文献求助10
6秒前
9秒前
9秒前
thy完成签到,获得积分10
9秒前
orixero应助霸气的思柔采纳,获得10
9秒前
12秒前
LWJJNU完成签到 ,获得积分10
13秒前
14秒前
14秒前
SciGPT应助人青采纳,获得10
15秒前
15秒前
15秒前
风中鹤发布了新的文献求助10
16秒前
胡辣椒麻鸡完成签到,获得积分10
16秒前
16秒前
艺心发布了新的文献求助10
18秒前
ggun发布了新的文献求助10
18秒前
汤泽琪发布了新的文献求助10
18秒前
vlots应助醉熏的伊采纳,获得30
19秒前
许子健发布了新的文献求助10
20秒前
yiyimx发布了新的文献求助10
20秒前
20秒前
情怀应助清脆的雨泽采纳,获得10
22秒前
23秒前
ww007完成签到,获得积分10
24秒前
zht完成签到,获得积分10
24秒前
JHJ完成签到,获得积分10
25秒前
666应助夕荀采纳,获得10
27秒前
圈圈发布了新的文献求助20
28秒前
ggun完成签到,获得积分10
29秒前
水濑心源发布了新的文献求助10
31秒前
33秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3966399
求助须知:如何正确求助?哪些是违规求助? 3511837
关于积分的说明 11160190
捐赠科研通 3246481
什么是DOI,文献DOI怎么找? 1793425
邀请新用户注册赠送积分活动 874438
科研通“疑难数据库(出版商)”最低求助积分说明 804388