基于流动复杂的三维曲线形状重建

ACM Trans. Graph. Pub Date : 2014-03-01 DOI:10.1145/2560328
Bardia Sadri, Karan Singh
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引用次数: 18

摘要

我们解决了从稀疏无组织的3D曲线集合中进行形状重建的问题,这些曲线通常是由日益流行的3D曲线素描应用程序生成的。实验中,我们观察到人类对连接的3D曲线形状的理解在很大程度上是一致的,这是由曲线的拓扑连通性和几何形状决定的。因此,我们在一种新颖的算法中使用流复合物(一种捕获输入拓扑和几何方面的结构)来产生无相交的3D三角形状,该形状可以插值输入3D曲线。我们的方法能够对高度非平面和凹曲线周期进行三角剖分,为具有挑战性的3D曲线输入提供鲁棒的3D网格和参数嵌入。我们的评估有四个方面:我们展示了我们的算法来匹配设计师选择的曲面周期;我们为广泛的曲线输入生产用户可接受的形状;我们证明了我们的方法是可预测的和健壮的曲线添加和删除;我们将我们的结果与现有技术进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flow-complex-based shape reconstruction from 3D curves
We address the problem of shape reconstruction from a sparse unorganized collection of 3D curves, typically generated by increasingly popular 3D curve sketching applications. Experimentally, we observe that human understanding of shape from connected 3D curves is largely consistent, and informed by both topological connectivity and geometry of the curves. We thus employ the flow complex, a structure that captures aspects of input topology and geometry, in a novel algorithm to produce an intersection-free 3D triangulated shape that interpolates the input 3D curves. Our approach is able to triangulate highly nonplanar and concave curve cycles, providing a robust 3D mesh and parametric embedding for challenging 3D curve input. Our evaluation is fourfold: we show our algorithm to match designer-selected curve cycles for surfacing; we produce user-acceptable shapes for a wide range of curve inputs; we show our approach to be predictable and robust to curve addition and deletion; we compare our results to prior art.
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