GAMES Webinar 2018-58期（Siggraph 2018论文）| 钟子春（美国韦恩州立大学），胡译心（纽约大学)

【GAMES Webinar 2018-58期（Siggraph 2018论文）】
报告嘉宾1：钟子春，美国韦恩州立大学
报告时间：2018年8月2日（星期四）晚8:00 – 8:45（北京时间）
主持人：刘洋，微软亚洲研究院（个人主页：https://xueyuhanlang.github.io/）
报告题目：Computing a High-Dimensional Euclidean Embedding from an Arbitrary Smooth Riemannian Metric
报告摘要：
This talk presents a new method to compute a self-intersection free high-dimensional Euclidean embedding for surfaces and volumes equipped with an arbitrary Riemannian metric. It is already known that given a high-dimensional (high-d) embedding, one can easily compute an anisotropic Voronoi diagram by back-mapping it to 3D space. We show here how to solve the inverse problem, i.e., given an input metric, compute a smooth intersection-free high-d embedding of the input such that the pullback metric of the embedding matches the input metric. Our numerical solution mechanism matches the deformation gradient of the 3D -> higher-d mapping with the given Riemannian metric. We demonstrate the applicability of our method, by using it to construct anisotropic Restricted Voronoi Diagram (RVD) and anisotropic meshing, that are otherwise extremely difficult to compute. In the embedding space constructed by our algorithm, difficult 3D anisotropic computations are replaced with simple Euclidean computations, resulting in an isotropic RVD and its dual mesh on this high-d embedding. Results are compared with the state-of-the-art in anisotropic surface and volume meshings using several examples and evaluation metrics.
讲者简介：
钟子春博士是美国韦恩州立大学计算机科学系助理教授，计算机建模与图像可视化实验室主任。他博士毕业于德克萨斯大学达拉斯分校，研究生和本科学位毕业于电子科技大学。他的主要研究方向是：计算机图像学，几何建模（曲面和体的网格生成），医学图像处理，可视化和GPU算法。他已在相关领域的高级杂志和会议上发表了近40篇论文，如：SIGGRAPH, TOG, VIS, TVCG, SGP, PG, CGF, SPM, CAD, GMP, CAGD, GM, CVM, ICCV, MICCAI, PMB, 等。并独立主持美国国家科学基金委科研项目。他还担任多个相关领域的高级期刊及会议论文评委，美国国家科学基金委评委，国际会议技术委员会等。
讲者个人主页：http://www.cs.wayne.edu/zzhong/

报告嘉宾2：胡译心，纽约大学
报告时间：2018年8月2日（星期四）晚8:45 – 9:30（北京时间）
主持人：刘洋，微软亚洲研究院（个人主页：https://xueyuhanlang.github.io/）
报告题目：Tetrahedral Meshing in the Wild
报告摘要：
We propose a novel tetrahedral meshing technique that is unconditionally robust, requires no user interaction, and can directly convert a triangle soup into an analysis-ready volumetric mesh. The approach is based on several core principles: (1) initial mesh construction based on a fully robust, yet efficient, filtered exact computation (2) explicit (automatic or user-defined) tolerancing of the mesh relative to the surface input (3) iterative mesh improvement with guarantees, at every step, of the output validity and increases in mesh quality. The quality of the resulting mesh is a direct function of the target mesh size and allowed tolerance: increasing allowed deviation from the initial mesh and decreasing the target edge length both lead to higher mesh quality.
Our approach enables “black-box” analysis, i.e. it allows to automatically solve partial differential equations on geometrical models available in the wild, offering a robustness and reliability comparable to, e.g., image processing algorithms, opening the door to automatic, large-scale processing of real-world geometric data.
讲者简介：
胡译心是纽约大学科朗数学研究所计算机学院的博士生，博导是Daniele Panozzo教授。曾在浙江大学计算机学院就读本科，并于2016年取得本科学位。她目前的研究方向是几何处理以及网格化问题。
讲者个人主页：https://cs.nyu.edu/~yixinhu/

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