Vector field processing on triangle meshes

F. D. Goes, M. Desbrun, Y. Tong
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引用次数: 66

Abstract

While scalar fields on surfaces have been staples of geometry processing, the use of tangent vector fields has steadily grown in geometry processing over the last two decades: they are crucial to encoding directions and sizing on surfaces as commonly required in tasks such as texture synthesis, non-photorealistic rendering, digital grooming, and meshing. There are, however, a variety of discrete representations of tangent vector fields on triangle meshes, and each approach offers different tradeoffs among simplicity, efficiency, and accuracy depending on the targeted application. This course reviews the three main families of discretizations used to design computational tools for vector field processing on triangle meshes: face-based, edge-based, and vertex-based representations. In the process of reviewing the computational tools offered by these representations, we go over a large body of recent developments in vector field processing in the area of discrete differential geometry. We also discuss the theoretical and practical limitations of each type of discretization, and cover increasingly-common extensions such as n-direction and n-vector fields. While the course will focus on explaining the key approaches to practical encoding (including data structures) and manipulation (including discrete operators) of finite-dimensional vector fields, important differential geometric notions will also be covered: as often in Discrete Differential Geometry, the discrete picture will be used to illustrate deep continuous concepts such as covariant derivatives, metric connections, or Bochner Laplacians.
三角网格的矢量场处理
虽然表面上的标量场一直是几何处理的主要内容,但在过去二十年中,切向量场的使用在几何处理中稳步增长:它们对于编码表面上的方向和大小至关重要,这在纹理合成、非真实感渲染、数字修饰和网格划分等任务中通常需要。然而,三角形网格上的切向量场有各种各样的离散表示,每种方法在简单性、效率和准确性之间提供了不同的权衡,这取决于目标应用程序。本课程回顾了用于设计三角形网格上矢量场处理计算工具的三种主要离散化方法:基于面、基于边和基于顶点的表示。在回顾这些表示所提供的计算工具的过程中,我们回顾了离散微分几何领域中向量场处理的大量最新发展。我们还讨论了每种离散化的理论和实践局限性,并涵盖了越来越常见的扩展,如n方向和n向量场。虽然本课程将着重于解释有限维向量场的实际编码(包括数据结构)和操作(包括离散算子)的关键方法,但重要的微分几何概念也将被涵盖:就像在离散微分几何中经常出现的那样,离散图像将用于说明深度连续概念,如协变导数、度量连接或Bochner拉普拉斯算子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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