通过隐性神经变形和特征引导切割进行逐步开发的建模
IEEE transactions on visualization and computer graphics
|September 28, 2023
概括
这项研究引入了一种自动方法,用于使用隐式神经表示来模拟具有可开发补丁的形状. 该方法优化了离散的可开发性和可制造性,在补丁数和近似误差之间实现了更好的平衡.
科学领域:
- 计算机图形 计算机图形
- 计算几何学的计算几何学
- 几何建模 几何建模
背景情况:
- 复杂形状的建模通常需要复杂的表面表示.
- 实现可开发表面对于制造和展开等制造工艺至关重要.
- 现有的方法可能会在图形独立性和分析曲率计算方面遇到困难.
研究的目的:
- 提出一种新的,自动的方法,用于形状建模,使用最小的离散可开发补丁集.
- 为了利用隐性神经形状表示来实现图形独立性和分析高斯曲率计算.
- 与最先进的方法相比,改善可开发补丁数量和近似误差之间的权衡.
主要方法:
- 使用隐性神经形状表示用于分析高斯曲率.
- 将输入形状变形为几乎可以开发的形状,具有突出的特征曲线.
- 将隐性场转换为三角形网格,沿特征曲线切割以实现磁盘拓.
- 交替优化离散的可开发性,可制造性约束和补丁合并.
主要成果:
- 证明了各种形状的可行性和实用性.
- 通过隐式表示实现了分析高斯曲率计算.
- 成功生成了可逐段开发的网格,具有优化的补丁数和近似误差.
结论:
- 拟议的方法提供了一种有效和自动的方式,可以使用可开发的补丁来建模形状.
- 隐式神经表示是实现图形独立性和分析曲率的关键.
- 该方法在近似精度和形状建模可开发补丁的数量之间提供了优越的权衡.
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