GAMES Webinar 2017-26期(Siggraph Asia 2017论文报告)| 董智超(中国科学技术大学),姜仲石(New York University)

【GAMES Webinar 2017-26期(Siggraph Asia 2017论文报告)】
报告嘉宾1: 董智超,中国科学技术大学
报告时间:2017年12月14日(星期四)晚20:00 – 20:45(北京时间)
主持人:宋鹏,洛桑联邦理工大学(个人主页:https://songpenghit.github.io/
报告题目:Smooth Assembled Mappings for Large-Scale Real Walking
报告摘要:
在虚拟现实的应用中真实行走能够给人提供更好的沉浸式体验。通过计算场景间的映射为用户提供了一种在小的物理空间中自由行走来漫游一个大的虚拟场景的可能。然而在虚拟场景与现实空间差别很大时,映射所造成的扭曲会严重影响到用户体验。我们提出了一种分而治之的方法,能够对于大尺度的虚拟场景计算出它与现实空间之间的低扭曲映射,从而能够为用户提供很好的漫游体验。项目主页:http://staff.ustc.edu.cn/~fuxm/projects/SAM/index.html
讲者简介:董智超,中国科学技术大学计算与应用数学系博士生,导师为刘利刚教授。2015年本科毕业于中国科学技术大学。主要兴趣方向为计算机图形学,虚拟现实等。

 

报告嘉宾2:姜仲石,New York University
报告时间:2017年12月14日(星期四)晚20:45 – 21:30(北京时间)
主持人:宋鹏,洛桑联邦理工大学(个人主页:https://songpenghit.github.io/
报告题目:Simplicial Complex Augmentation Framework for Bijective Maps
报告摘要:
Bijective maps are commonly used in many computer graphics and scientific computing applications, including texture, displacement, and bump mapping. However, their computation is numerically challenging due to the global nature of the problem, which makes standard smooth optimization techniques prohibitively expensive. We propose to use a scaffold structure to reduce this challenging and global problem to a local injectivity condition. This construction allows us to benefit from the recent advancements in locally injective maps optimization to efficiently compute large-scale bijective maps (both in 2D and 3D), sidestepping the need to explicitly detect and avoid collisions. Our algorithm is guaranteed to robustly compute a globally bijective map, both in 2D and 3D. To demonstrate the practical applicability, we use it to compute globally bijective single patch parametrizations, to pack multiple charts into a single UV domain, to remove self-intersections from existing models, and to deform 3D objects while preventing self-intersections. Our approach is simple to implement, efficient (orders of magnitude faster than competing methods), and robust, as we demonstrate in a stress test on a parametrization dataset with over a hundred meshes.
讲者简介:姜仲石是纽约大学科朗数学研究院计算机专业二年级博士生,本科毕业于中国科学技术大学少年班学院数学与应用数学专业,目前的研究方向主要是用数学优化和数据驱动方法的数字几何处理。讲者个人主页:http://cs.nyu.edu/~zhongshi/

 

报告的详细信息请见GAMES主页 https://games-cn.org
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观看直播的链接:http://webinar.games-cn.org

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