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Computing and regularizing medial axes in 3D
Computing and regularizing medial axes in 3D
教师介绍

本讲教师:居涛
所属学科:理科
人  气:310

课程介绍
报告摘要:Medial axes is a classical concept in computational geometry and has been the basis of most of today's skeletal shape descriptors.In this talk, I will discuss our recent work in addressing two major roadblocks in using medial axes for 3D shape analysis: the difficulty in computing the medial axes of general 3D shapes, and the sensitivity of the medial axes to noise. First, I will describe a novel voxel-based algorithm for computing 3D medial axes that is numerically robust, scalable to large volumes, simple to implement, and equipped with strong theoretical guarantees. Second, I will present a medial axes regularization method that is guided by a theoretically sound significance measure and capable of producing a family of skeletons that are descriptive and robust to noise. These works have been, or will be, presented at Siggraph (2016, 2018). Finally, I will briefly discuss some applications of skeletons in biomedicine that my lab is currently exploring. 报告人简介:Tao Ju is a Professor in the Department of Computer Science and Engineering at Washington University in St. Louis. He is currently serving as the Vice Dean of Research for the School of Engineering and Applied Science. He obtained his B.S. and B.A. degrees from Tsinghua University in 2000, and his M.S. and PhD degrees in Computer Science from Rice University in 2005. He conducts research in computer graphics and bio-medical applications, and is particularly interested in geometric modeling and shape analysis. He has served as an associated editor for IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Computer-Aided Design, Graphical Models, and Computational Visual Media. He has served on the program committees of all major conferences in computer graphics (Siggraph, Siggraph Asia, Eurographics, SGP, Pacific Graphics, etc.), and has chaired, or will chair the committees of Pacific Graphics (2007), SGP (2018), and GMP (2019). His research is funded by NSF and NIH, including an NSF CAREER award in 2009. Homepage:http://www.cs.wustl.edu/~taoju/

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