GAMES Webinar 2019 – 104期 （几何专题课程）|辛士庆（山东大学），王斌（清华大学）
【GAMES Webinar 2019-104期】
报告嘉宾：辛士庆，山东大学
报告时间：2019年7月25日 晚8:00-8:45（北京时间）
主持人：张举勇，中国科学技术大学（个人主页：http://staff.ustc.edu.cn/~juyong/）
报告题目：与标量场相适应的网格剖分及其在测地等值线和数值积分中的应用
报告摘要：
在传统中的数字几何处理中，人们常常把离散标量场看作是附着在某个离散网格上的离散函数。事实上，标量场和网格化之间存在相辅相成的关系。报告将首先基于质心Voronoi图、质心Power图讨论受标量场驱动的剖分形式。然后，报告将讨论给定一个标量场，何种网格化能够产生对标量场的更好离散表达。两个特殊的应用分别是测地等值线的抽取和网格曲面上的数值积分。
讲者简介：
辛士庆，2009年取得浙江大学数学系博士学位。2009-2012年，在新加坡南洋理工大学继续深造。2012-2017年，在宁波大学工作。目前是山东大学计算机学院交叉研究中心副教授。研究方向是计算图形学、计算几何。主持国家自然科学基金2项，发表论文60余篇，其中CCF推荐A类文章14篇(含ACM TOG 8篇)，B类论文20余篇，还有1项美国发明专利，两次获得ACM SPM最佳论文奖。已经培养毕业的博士3名，硕士11名。目前被中国海洋发展研究中心聘请为研究员，被中国科学院（宁波材料所）聘请为兼聘副教授，被青岛达芬奇科技有限公司聘请为特聘专家。
讲者个人主页：https://www.xinshiqing.com/
报告嘉宾：王斌，清华大学软件学院
报告时间：2019年7月25日 晚8:45-9:30（北京时间）
主持人：张举勇，中国科学技术大学（个人主页：http://staff.ustc.edu.cn/~juyong/）
报告题目：Surface Reconstruction Based on Modified Gauss Formula
报告摘要：
We introduce a surface reconstruction method that has excellent performance despite non-uniformly-distributed, noisy, and sparse data. We reconstruct the surface by estimating an implicit function and then obtain a triangle mesh by extracting an iso-surface. Our implicit function takes advantage of both the indicator function and the signed distance function. The implicit function is dominated by the indicator function at the regions away from the surface and is approximated (up to scaling) by the signed distance function near the surface. On one hand, the implicit function is well defined over the entire space for the extracted iso-surface to remain near the underlying true surface. On the other hand, a smooth iso-surface can be extracted using the marching cubes algorithm with simple linear interpolations due to the properties of the signed distance function. Moreover, our implicit function can be estimated directly from an explicit integral formula without solving any linear system. An approach called disk integration is also incorporated to improve the accuracy of the implicit function. Our method can be parallelized with small overhead and shows compelling performance in a GPU version by implementing this direct and simple approach. We apply our method to synthetic and real-world scanned data to demonstrate the accuracy, noise resilience, and efficiency of this method. The performance of the proposed method is also compared with several state-of-the-art methods.
讲者简介：
Bin Wang is currently an associate professor at School of Software, Tsinghua University, China. He received his B.Sc. degree in Chemistry in 1999, and his Ph.D. in Computer Science from Tsinghua University in 2005. He was a research assistant at Department of Computer Science, Hong Kong University, and had postdoctoral research training at ISA/ALICE Research Group, INRIA-LORIA, France. His research interests include digital geometry processing, non/- photorealistic rendering, and image and video processing.
讲者个人主页：http://cgcad.thss.tsinghua.edu.cn/wangbin/
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