USTC Summer School 2017 (001M0601)

Advances in Computer Graphics


图形与几何计算实验室 (Graphics&Geometric Computing Laboratory)

中国科学技术大学 (University of Science and Technology of China)


课程介绍        课程安排        授课教师及课程介绍       历年暑期课程


  • 2017年7月9日:暑期课程视频

  • 2017年7月7日:中科大的选课的本科生的课程考核要求(点击进入),deadline:2017年8月27日

  • 2017年6月15日:已通过SmartChair系统向成功注册的学员发送邮件。请成功注册课程的学员仔细查看课程注意事项(点击查看)。

  • 注册人数已满,不再接受新注册。

  • 2017年5月30日:参加课程注意事项(点击查看)。

  • 2017年5月30日:已通过系统向所有选课学员发送了选课通知,请各位选课学员查看邮箱确认是否已成功选课,请注意查看信箱。

  • 2017年5月29日:注册人数已满,不再接受注册。

  • 2017年5月9日:参加暑期课程的学员食宿自理。中国科技大学东校区周边酒店信息下载( 供参考,具体信息请直接联系酒店)。由于科大暑期活动很多,周边住宿会非常紧张。请外地学员及早自行预订住宿。

  • 2017年5月9日:课程地点在中国科技大学东区的理化大楼,靠近科大东区南门(太湖路)附近,点击查看地图位置

  • 2017年5月4日:课程开放注册。点击注册注册之前请务必仔细查阅课程注册说明文档”)。 注册名额有限。名额满后,即不再开放注册。

  • 2017年4月15日:注册系统将在5月初开放。名额有限,敬请关注。

  • 2017年2月26日:今年的暑期课程时间定为2017年7月3日至7月7日共5天。授课计划初步确定,今年的课程将继续完全免费。



  • 《计算机图形学前沿进展》(课程编号:001M06)为中国科技大学暑期学期的课程。课程由数学科学学院中科大图形与几何计算实验室(GCL)刘利刚老师及国内外学者共同授课。本年度课程的主题为“3D几何感知与建模、 虚拟现实、机器人与机器学习”。若对计算机图形学中的几何处理不太熟悉的同学,可提前看一下 刘利刚老师开设的本科生课程《计算机图形学》(20132014201520162017)和研究生课程《数字几何处理》的主页(其中有较完善的课程课件提供下载 )

  • 该课程为中国科学技术大学全校性公共选修课程,面向应用数学、计算机科学、信息科学等相关专业的学生,欢迎数学学院、少年班学院、信息学院、计算机学院等学院的本科生高年级学生和研究生来选课 。

  • 若本校的本科生需要该课程的学分,需要在校教务系统中进行选课。

  • 该课程以介绍计算机图形学领域的最新的研究成果及进展为主,同时兼顾本科生也会介绍该领域的一些基本问题和研究方向,只要有《线性代数》、《微积分》、《解析几何》、《微分几何》等课程知识的学生都可以听懂。

  • 本次课程的内容涵盖几何建模、网格化技术、形状的感知分析、点云处理、深度相机、细分造型技术、3D打印、虚拟现实 、机器人、深度学习等内容,内容丰富和前沿,是了解计算机图形学前沿和未来方向的非常难得的机会。

  • 上课时间:2017年7月3日至7月7日

  • 上课地点:中国科学技术大学东区理化大楼西三报告厅

  • 学分:2









City& Modeling





陈宝权 城市场景三维感知与分析


Long Quan Capturing the World with Cameras!


王程 大规模移动激光扫描三维点云获取与处理


Hongbo Fu Sketch-based 3D Modeling





周而进 深度学习下人脸识别的应用与挑战


张举勇 三维人脸重建及其应用


谢国富 3D VR视频合成及应用


傅孝明 Real Walking Mapping Computation and Its Applications in VR





林宙辰 Low-Rank Subspace Clustering


沈小勇 Awesome Applications of Deep Learning in Computer Vision


韩晓光 深度学习在三维重建及建模中的应用


刘利刚 面向制造的几何设计与优化





童欣 Data-Driven graphics


邵天甲 基于常见图像类型的三维建模


黄晓煌 计算机图形学与GPGPU在“酷家乐”里的实践


Renjie Chen Parallel geometry processing on GPU





卢策吾 Autonomous Machine powered by Computer Vision


郭诗辉 面向机器人的动态结构设计


宋鹏 智能机器人视觉感知中的三维几何分析与处理


Paul Kry Physics Based Computer Animation Fundamentals




  • Paul G. Kry, McGill University, Canada

    Paul G. Kry received his B.Math. in computer science with electrical engineering electives in 1997 from the University of Waterloo, and his M.Sc. and Ph.D. in computer science from the University of British Columbia in 2000 and 2005. He spent time as a visitor at Rutgers during most of his Ph.D., and did postdoctoral work at INRIA Rhône Alpes and the LNRS at Université René Descartes. He is currently an associate professor at McGill University, where he heads the Computer Animation and Interaction Capture Laboratory. His research interests are in physically based animation, including deformation, contact, motion editing, and simulated control of locomotion, grasping, and balance. He co-chaired ACM/EG Symposium on Computer Animation in 2012, Graphics Interface in 2014, and served on numerous program committees, including ACM SIGGRAPH, SIGGRAPH ASIA, Eurographics, ACM/EG Symposium on Computer Animation, Pacific Graphics, and Graphics Interface. He is currently an associate editor for Computer Graphics Forum, and for Computers and Graphics. In 2014, Paul Kry became the president of the Canadian Human Computer Communications Society, the organization which sponsors the annual Graphics Interface conference, and since 2016 he is a director at large on the ACM SIGGRAPH executive committee.


  • Title: Physics Based Computer Animation Fundamentals
    This 90 minute course will introduce the main concepts of physically based computer animation, with a focus on elastic and rigid body systems. We will examine different numerical integration techniques and see how the eigenvalue decomposition of a linear system can be used to understand the long term behavior of different stepping methods. We will look at real examples of larger stiff systems that benefit from iterative linear solvers, and see how constraints and constraint stabilization work as an alternative. Collision detection, collision response, contact constraints and friction will also be covered. Various concepts will be demonstrated with interactive 2D examples. Additional resources for future study will be included throughout the course. To conclude I will briefly point out several advanced topics that build on this material.

  • Long QUAN,香港科技大学(HKUST)

    Long QUAN is a Professor of the Department of Computer Science and Engineering at HKUST. He received his PhD in 1989 in Computer Science from INPL, France. He became a permanent researcher at CNRS in 1990 and was appointed to INRIA in Grenoble, France. He joined HKUST in 2001, and was the founding director of the HKUST Center for Visual Computing and Image Science. He is a Fellow of the IEEE Computer Society. He founded the altizure.com.

    He works on vision geometry, 3D reconstruction and image-based modeling. He was voted one of the HKUST Best Ten Lecturers in 2004 and 2009. He has served as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence, and is a Regional Editor of Image and Vision Computing Journal. He is on the editorial board of the International Journal of Computer Vision, Electronic Letters on Computer Vision and Image Analysis, Machine Vision and Applications, and Foundations and Trends in Computer Graphics and Vision. He was a Program Chair of IAPR International Conference on Pattern Recognition 2006 Computer Vision and Image Analysis, a Program Chair of ICPR 2012 Computer and Robot Vision, and a General Chair of IEEE International Conference on Computer Vision (ICCV) 2011.


  • Title: Capturing the World with Cameras!
    Professor Quan leads a team that uses photographs and deep visual learning technologies to produce complete 3D models of all types of locations on Earth. In this talk, he reviews the developments in computer vision and machine learning over the past three decades. He also turns the focus on recent exciting work in deep visual learning and 3D reconstruction using photographs. Here, he showcases the approach using case studies of both large scale of hundreds of square kilometers from drone photographs, and also the small-scale 3D objects from smart phone photographs. He also demonstrates the online cloud platform and portal www.altizure.com with its crowed-sourced Altizure Earth, developed and funded by the HKUST team, rivaling the popular Google earth!

  • 陈宝权,山东大学

    陈宝权,山东大学计算科学与技术学院与软件学院院长、长江学者特聘教授。研究领域为计算机图形学与数据可视化,973项目“城市大数据计算理论与方法”首席科学家。获电子工程学士、硕士,和计算机科学博士(纽约州立大学石溪分校)。担任IEEE VIS指导委员会委员;曾任ACM SIGGRAPH ASIA指导委员会委员和2014年大会主席、IEEE Visualization 2005 会议主席和2004 年程序委员会主席。获2003 美国 NSF CAREER Award(美国科学基金杰青),2008年中国科学院“百人”计划,2010年国家杰出青年基金获得者,2014年"中国计算机图形学大会杰出奖"。任中国计算机学会常务理事。


  • Title: 城市场景三维感知与分析

  • 王程, 厦门大学

    王程,厦门大学信息科学与技术学院教授、副院长,福建省智慧城市感知与计算重点实验室常务副主任。研究方向为三维空间感知、三维媒体技术、遥感数据智能处理,激光雷达数据处理、城市计算。现为国际摄影测量与遥感学会(ISPRS)多传感器集成与融合工作组主席,中国图像图形学会常务理事理事,CCF YOCSEF厦门分论坛创始主席,IEEE高级会员。主持过国家863、部委型号、国家自然科学重点基金等数十项。拥有发明专利8项。获省部级科技奖奖5项,863 先进个人奖1 项。在IEEE TGRS、TITS等期刊和AAAI等会议上发表论文150余篇。2014年起在ISPRS的国际移动测量暑期学校中主讲大规模点云处理。组织了IEEE和ISPRS学会的多个国际学术会议,是多个IEEE 会刊和ISPRS期刊的长期审稿人和客座编辑。


  • Title: 大规模移动激光扫描三维点云获取与处理

  • 童欣,微软亚洲研究院

    童欣博士现为微软亚洲研究院首席研究员,网络图形组研究主管。研究领域为计算机图形和计算机视觉,主要研究方向为材质建模,真实感绘制,纹理合成,人脸动画,以及几何处理等。现任学术期刊ACM TOG,IEEE TVCG,CVM,计算机辅助设计与制造编委,曾任CGF编委, PG 2013论文主席,与多届SIGGRAPH ASIA,SIGGRAPH,Eurographics,PG论文委员会成员。


  • Title: Data Driven Graphics

  • 林宙辰,北京大学

    Zhouchen Lin received the Ph.D. degree in applied mathematics from Peking University in 2000. He is currently a Professor at Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University. He is also a Chair Professor at Northeast Normal University and a guest professor at Beijing Jiaotong University. He was a guest professor at Shanghai Jiaotong University and Southeast University, and a guest researcher at Institute of Computing Technology, Chinese Academy of Sciences. His research areas include image processing, computer vision, pattern recognition, machine learning, and numerical optimization He is an associate editor of IEEE T. Pattern Analysis and Machine Intelligence and International J. Computer Vision and an area chair or senior program committee member of CVPR 2014, ICCV 2015, NIPS 2015, AAAI 2016, CVPR 2016, IJCAI 2016, AAAI 2017, and AAAI 2018. He is an IAPR Fellow.


  • Title: Low-Rank Subspace Clustering
    Low-rank modeling are emerging mathematical tools dealing with uncertainties of real-world visual data. Leveraging on the underlying structure of data, low-rank modeling approaches have achieved impressive performance in many visual learning tasks. In this tutorial, I will introduce in detail the latest subspace clustering techniques. For low-rankness based approaches, I will present some representative subspace clustering models, analyze their theoretical properties, such as exact recovery, closed-form solutions and block-diagonal structure, and present applications in various real problems in computer vision.

  • Hongbo Fu, City University of Hong Kong

    Hongbo Fu is an Associate Professor in the School of Creative Media, City University of Hong Kong. Before joining CityU, he had postdoctoral research trainings at the Imager Lab, University of British Columbia, Canada and the Department of Computer Graphics, Max-Planck-Institut Informatik, Germany. He received the PhD degree in computer science from the Hong Kong University of Science and Technology in 2007 and the BS degree in information sciences from Peking University, China, in 2002. His primary research interests fall in the fields of computer graphics and human computer interaction. His research has led to more than 50 scientific publications, including over 10 technical papers presented at SIGGRAPH or SIGGRAPH Asia. His recent works have received the Best Demo awards at the Emerging Technologies program, SIGGRAPH Asia in both 2013 and 2014, and the Best Paper award from CAD/Graphics 2015. He was the Organization Co-Chair of Pacific Graphics 2012, the Program Chair/Co-chair of CAD/Graphics 2013, SIGGRAPH Asia 2013 (Emerging Technologies), SIGGRAPH Asia 2014 (Workshops) and CAD/Graphics 2015, and the Conference Chair of SIGGRAPH Asia 2016. He is currently on the SIGGRAPH Asia Conference Advisory Group. He has served as an Associate Editor of The Visual Computer (2013-2016), Computers & Graphics, and Computer Graphics Forum.


  • Title: Sketch-based 3D Modeling
    3D modeling has been one of the fundamental research topics in computer graphics and computer vision. While at least hundreds of 3D modeling techniques have been proposed in the past decades, most of the existing techniques have a high learning curve and are limited to professionals. On the other hand, the emerging applications such as Augmented Reality and 3D printing demand 3D modeling tools that are easy to learn and use. Sketch-based interfaces potentially enable such modeling tools, since sketches as simple forms can depict complicated ideas. In this talk, I will give an overview of sketch-based 3D modeling and focus more discussions on my own attempts and the state-of-the-art works.

  • 卢策吾,上海交通大学

    Cewu Lu (卢策吾) is a Research Professor at Shanghai Jiao Tong University (SJTU) and leader of Vision Machine and Intelligence Group. Before he joined SJTU, he was a research fellow at Stanford University AI lab working under Prof. Fei-fei Li and Prof. Leonidas J. Guibas . He was a Research Assistant Professor at Hong Kong University of Science and Technology with Prof. Chi Keung Tang . He got the his PhD degree from The Chinese University of Hong Kong, supervised by Prof. Jiaya Jia. He was selected as the 1000 Overseas Talent Plan (Young Talent) (中组部青年千人计划) by Chinese central government. He has published about 30 CCF-A papers (including CVPR/ICCV/TPAMI/IJCV). He is one of core technique member in Stanford-Toyota autonomous car project (斯坦福-丰田,无人车项目). Some of his proposed algorithms have been used as a basic tool function in OpenCV (such as decolor.cpp). He has one Best Paper Award at the Non-Photorealistic Animation and Rendering (NPAR) 2012 and one most cited paper among all papers in SIGGRAPH recent 5 years. He serves as an associate editor for Journal gtCVPR and reviewer for Journal TPAMI and IJCV. His research interests fall mainly in Computer Vision, deep learning, deep reinforcement learning and robotics vision.


  • Title: Autonomous Machine powered by Computer Vision
    Autonomous Machines (e.g. self-driving car, drone and robot arm) keep arising as computer vision technology develops. In this talk, how computer vision powers autonomous machine will be introduced, including better perception, better auto-planning and better knowledge. For better perception, some recent advanced deep learning and 3D vision technologies will be presented. For better auto-planning, deep reinforcement learning (including imitation learning) will be introduced. Finally, we will revisit some recent important knowledge-based project in computer vision, such as, visual genome and ShapeNet to discuss how to apply visual knowledge to improve both perception and auto-planning. Finally, we focused on the relationship among perception, auto-planning and knowledge and possible future directions.

  • 陈仁杰,德国马克斯普朗克计算机所

    陈仁杰,德国马克斯普朗克计算机所研究员。2005年于浙江大学获得学士学位,2010年于浙江大学获得应用数学专业博士学位。2011年至2015年于以色列理工大学和美国北卡罗来纳大学教堂山分校从事博士后研究,2015年至今工作于德国马普计算机所。研究领域为计算机图形学,主要研究方向包括几何处理和建模、计算几何及裸眼3D显示器等。近年来在SIGGRAPH, Eurographics, SGP, TVCG等国际重要期刊及会议上发表学术论文多篇。


  • Title: Parallel geometry processing on GPU
    It has been over a decade since the CPU industry shifted its focus towards the multi-core architecture, however many existing and newly developed algorithms in computer graphics and geometry processing are still sequential in nature, which makes it difficult to unleash the parallel computation powers of modern multi-core CPUs. In this talk, we will discuss how to take the parallel computing viewpoint and re-design algorithms for some geometry processing problems. Moreover, we will talk about how to implement algorithms on massively parallel GPUs for computationally expensive while performance demanding applications.

  • 邵天甲,浙江大学

    邵天甲,浙江大学CAD&CG国家重点实验室的助理研究员。他博士师从微软亚洲研究院副院长郭百宁教授,于2014年1月在清华大学高等研究院获得计算机博士学位。此前他于2008年在清华大学自动化系获得学士学位。在加入浙大之前,他于2008年9月至2012年8月在微软亚洲研究院IG组作为实习生长期参与研究工作。2012年8月至2013年6月,他作为访问学生在英国伦敦大学学院和Niloy Mitra教授合作参与研究工作。邵天甲的研究兴趣主要包括基于图像的三维建模,室内场景和物体的扫描、建模与分析,并在图形学顶级会议和期刊ACM Siggraph (Asia)和IEEE TVCG上发表多篇论文。


  • Title: 基于常见图像类型的三维建模

  • 郭诗辉,厦门大学

    郭诗辉,厦门大学研究型助理教授。2010年本科毕业于北京大学元培学院;2010年至2015年期间于英国伯恩茅斯大学英国国家计算动画中心攻读博士学位;2015年至2016年期间在新加坡南洋理工Prof. Nadia Thalmann课题组从事社交机器人相关工作。相关工作发表于《Computer Graphics Forum》等国际期刊。研究领域包括动画角色的运动仿真、机器人的自动化设计等。


  • Title: 面向机器人的动态结构设计

  • 沈小勇,香港 中文大学

    Xiaoyong Shen got his Ph.D. degree in Computer Science and Engineering Department in the Chinese University of Hong Kong in 2016. His supervisor is Prof. Jiaya Jia. Before that, he received the B. S. degree in Computational Mathematics and M. S. degree in Applied Mathematics from Zhejiang University in 2010 and 2012 respectively, under the supervision of Prof. Ligang Liu. His research interest includes computer graphics and computer vision.


  • Title: Awesome Applications of Deep Learning in Computer Vision
    Recently, the algorithms based on deep leaning have achieved the state-of-the-art performance in many computer vision applications including object detection, image classification, image segmentation, image generation, etc. In this talk, a set of typical and successful applications of deep learning in computer vision will be presented. The understandings of deep learning frameworks for different problem will be also illustrated. Lastly, I will show some product-level deep learning solutions from our research group and the underlying tricks will be also analyzed.

  • 韩晓光,香港大学

    韩晓光,香港大学博士在读(2013年-至今,即将毕业),2009年本科毕业于南京航空航天大学数学系,2011年于浙江大学应用数学专业获得硕士学位,并于2011年至2013年间于香港城市大学数字媒体学院任研究助理。自2009年开始,韩晓光一直从事计算机图形学,计算机视觉以及计算几何方面的研究工作,主要包括图像和视频的编辑,三维建模,本征图分解以及三维网格上的测地计算等。韩晓光目前的主要研究兴趣在基于深度学习技术的三维模型获取。他在该领域取得了一定的成果,并均发表在计算机图形学领域的重要国际期刊及会议上,包括ACM SIGGRAPH/SIGGRAPH Asia, IEEE CG&A, Graphical Models等。


  • Title: 深度学习在三维重建及建模中的应用

  • 黄晓煌,"酷家乐”,创始人兼董事长

    黄晓煌,第十三批 “国家千人计划”入选,本科毕业于浙江大学竺可桢学院, 计算机图形学方向,导师鲍虎军;获得伊利诺伊大学香槟分校(UIUC)全额奖学金,导师 Wenmei-Hwu 院士, 从事GPGPU领域研究,获得硕士学位。 在美国英伟达Nvidia担任软件工程,主要参与CUDA语言开发。其创办的酷家乐(www.kujiale.com)是以GPGPU分布式并行计算和计算机图形学为技术核心,推出的VR智能室内设计平台。于2013年11月上线,10秒生成效果图,5分钟生成装修方案。2016年,公司估值达3亿美金,团队400多人。


  • Title: 计算机图形学与GPGPU在“酷家乐”里的实践
    “酷家乐”在研发过程中,应用了大量的图形学和GPGPU并行计算的知识。该课程会简单介绍一下图形学知识,包括全局光照,模型处理,图像处理,图像识别,GPGPU用在全局光照及深度学习里的应用等。包括会重点介绍下CUDA及Intel ISPC等GPGPU编程语言在实际互联网产品中应用的实战经历。

  • 周而进,北京旷视科技有限公司(Face++),高级研究员



  • Title: 深度学习下人脸识别的应用与挑战

  • 谢国富,北京冰立方科技有限公司,联合创始人&CTO

    谢国富,北京冰立方科技有限公司联合创始人&CTO。2007年本科毕业于厦门大学,2013年在中科院软件所获得博士学位。2013年至2014于加拿大蒙特利尔大学从事博士后研究。研究领域为计算机图形学和计算机视觉,主要研究方向为真实感渲染、矢量化、三维重建等。相关研究成果发表在计算机图形学领域的重要国际期刊和会议上,包括SIGGRAPH、SIGGRAPH Asia、TVCG等。曾在百度自动驾驶,360人工智能研究院工作。2016年联合创办冰立方VR,公司以视觉图形及分布式计算为核心技术,推出3D VR视频合成及处理云引擎。


  • Title: 3D VR视频合成及应用
    随着头盔等VR设备的逐渐普及,人们对VR内容有更多的需求,特别是高质量强沉浸感的3D VR视频。目前VR视频制作成本居高不下,如何自动及低成本地生成高质量的VR内容是一个大的难点,也是冰立方致力于解决的问题。本课程将介绍3D VR视频合成原理,后期防抖特效处理,场景漫游购物等应用,并将介绍最新研究进展和冰立方的解决方案。

  • 张举勇,中国科学技术大学

    张举勇,中国科学技术大学副教授。2006 年于中科大计算机系获得学士学位,2011 年于新加坡南洋理工大学计算机工程学院获得博士学位,2011 年至2012 年于瑞士联邦理工学院从事博士后研究,2012 年至今工作于中国科学技术大学数学科学学院。研究兴趣包括计算机图形学、计算机视觉、最优化算法等。现作为项目负责人主持研究国家重点研发计划子课题一项,国家自然科学基金面上项目一项。近几年来,在国际一流学术期刊如T-PAMI,ACM-TOG,TVCG上发表多篇研究论文。


  • Title: 三维人脸重建及其应用
    In recent years, face related tasks including face alignment and face recognition have achieved great success thanks to the deep learning. However, due to the limitations caused by the image formation process, the 2D face recognition is still sensitive to large view, challenging expressions and lighting conditions. On the other hand, the face recognition system is required to be robust to extremely challenging conditions in real applications. In this talk, I will introduce our recent 3D assisted face analysis works including 3D face reconstruction from image, normalization based face recognition, and the experimental results demonstrates the improvements over traditional 2D approaches.

  • 宋鹏,中国科学技术大学

    主要研究方向为计算机图形学、计算机视觉、人机交互。近年来在SIGGRAPH, SIGGRAPH Asia, TVCG,


  • Title: 智能机器人视觉感知中的三维几何分析与处理

  • 傅孝明,中国科学技术大学

    傅孝明,中国科学技术大学数学学院副研究员,2011年本科毕业于中国科学技术大学自动化专业,2016年6月在中国科学技术大学自动化系获得博士学位。自2012年起,傅孝明一直从事计算机图形学与数字几何处理方面的研究,主要是网格生成、映射计算及其在VR中的应用。在各向异性网格生成、最优映射计算、PolyCube构造(六面体网格生成)方面取得了一定的成果,相关论文均发表在计算机图形学领域的重要国际期刊和会议上,包括SIGGRAPH、SIGGRAPH Asia、TVCG等。


  • Title: Real Walking Mapping Computation and Its Applications in VR
    在VR应用中为了提供沉浸式的体验,将实时感渲染和real walking结合起来是一个很不错的方案。实时感渲染通过现有的HMD已经能实现,但是real walking存在一个局限:不能在小的实际空间中去体验大的虚拟场景。为此,有很多的方法被提出,包括space manipulation, physical prop, redirected walking and warpped space。在本课程中,我首先会综述性地介绍这些相关技术,然后介绍warpped space方法的算法流程,最后介绍我们的基于warpped space的且适合大场景映射的方法。

  • 刘利刚,中国科学技术大学

    刘利刚,中国科学技术大学教授,博士生导师,中国科学院“百人计划”,中国科学院特聘研究员。于2001年在浙江大学获得应用数学博士学位;2001年至2004年期间在微软亚洲研究院工作;2004年至2011年期间在浙江大学数学系工作。2009年至2011年期间,在美国哈佛大学进行学术访问研究。研究兴趣包括计算机图形学,3D几何建模与处理,3D打印中的几何优化等。主持国家自然科学基金项目4项,2012年获得国家自然科学“优秀青年基金”项目。获得国家发明专利2项,计算机软件著作权15项。获得“微软青年教授”奖(2006)、陆增镛CAD&CG高科技奖一等奖(2010)、国家自然科学奖二等奖(2013)、中科大校友基金会青年教师事业奖(2014)、中国科大—兴业证券教育奖(2016)等奖项。国际会议GMP 2017大会共同主席,SPM 2014, SGP 2015, CVM 2016, CAD/Graphics 2017的论文共同主席。学术期刊IEEE TVCG, IEEE CG&A, CGF, CAGD, The Visual Computer及《软件学报》编委。中国计算机学会计算机辅助设计与图形学专业委员会常务委员,中国工业与应用数学学会几何设计与计算专业委员会秘书长。


主办单位:中国科学技术大学         协办单位:中国图像图形学学会





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