报告时间：2016年4月18日（星期一）
报告地点：浙江大学紫金港校区蒙民伟楼CAD&CG国家重点实验室402室
报告人：张皓 教授
主持人：吴鸿智 博士
报告一：4月18日10:00
TITLE: Pyramids, Tetris, and Spirals: New Geometry Problems for 3D Printing
ABSTRACT: Recent advances in 3D printing technologies have piqued the interests of the computer graphics community, as the desire to improve efficiency and quality of 3D fabrication is shedding new lights on several classic geometry problems such as shape approximation, decomposition, and packing. In this talk, I will cover several new geometry problems we have come across and tackled in recent years that are strongly tied to 3D printing. However, what started our initial investigation were often the intrigue and potential generality of the geometry problems, beyond their specific applications to 3D printing. I will show you how pyramid-like shapes, the Tetris game, and spirals, in particular the lesser known Fermat spirals, all have interesting and surprising connections to the geometry of 3D printing. It will be encouraging to see that what we could offer are all first solutions and there is great room to improve. Given time, I can also talk about a few other problems we studied for computational design.
报告二：4月18日14:30
TITLE: Why is Computer Graphics Hard?
ABSTRACT:Computer graphics is traditionally defined as a field which covers all aspects of computer-assisted image synthesis. Is computer graphics hard? An introductory class to graphics mainly teaches how to turn an explicit model description including geometric and photometric attributes into one or more images. Under this classical and arguably narrow definition, computer graphics corresponds to a “forward'' (synthesis) problem, which is probably easier than the inverse (analysis) problem, one which computer vision traditionally battles with.
In this talk, before offering my new perspectives, let me first remind ourselves several well-known data challenges that are unique to graphics problems. Then, by altering the above classical definition of computer graphics, perhaps only slightly,I show that to do the synthesis right, one has to first solve various inverse problems. In this sense, graphics and vision are converging, with data and learning playing key roles in both fields. A recurring challenge however is a general lack of “Big 3D Data”, which graphics research is expected to address. Finally, I want to explore a new perspective for the synthesis problem to mimic a higher-level human capability than pattern recognition and understanding.
I hope to convince you that under these new views of the field, and as we explore perhaps its boundary, computer graphics can be pretty hard, and we are only starting to scratch the surface.
