实验室网站

题    目：Introduction of Point Cloud Compression 时    间：2017年11月6日9:00 a.m. 地    点：浙江大学玉泉校区信电楼215学术报告厅 报 告 人：Dr. Huifang Sun，IEEE Fellow, MERLFellow

专家介绍:

Dr. Huifang Sun graduated from Harbin Military Engineering Institute, Harbin, China, and received the Ph.D. from University of Ottawa, Canada. He was an Associate Professor in Fairleigh Dickinson University in 1990. He joined to Sarnoff Corporation in 1990 as a member of technical staff and was promoted to a Technology Leader of Digital Video Communication. In 1995, he joined Mitsubishi Electric Research Laboratories (MERL) and was promoted as Vice President and Deputy Director in 2003 and currently is a Fellow of MERL. He has co-authored two books and published more than 150 Journal and Conference papers. He holds more than 61 US patents. He obtained the Technical Achievement Award for optimization and specification of the Grand Alliance HDTV video compression algorithm in 1994 at Sarnoff Lab. He received the best paper award of 1992 IEEE Transaction on Consumer Electronics, the best paper award of 1996 ICCE and the best paper award of 2003 IEEE Transaction on CSVT. He was an Associate Editor for IEEE Transaction on Circuits and Systems for Video Technology and was the Chair of Visual Processing Technical Committeeof IEEE Circuits and SystemSociety.He is an IEEE Fellow.

报告内容摘要：

With recent advances of 3D technologies, there are now more and more devices that can capture and present 3D representations of the world. A point cloud is a set of points in a 3D space each with associated attributes, e.g. color, material properties, etc. Point clouds can be used to reconstruct an object or a scene as a composition of such points. Therefore, the point cloud has become a potential and practical format to represent 3D data in many applications, such as virtual reality, mobile mapping, scanning of historical artifacts, 3D printing and digital elevation models. The point clouds can be captured using 3D scanner or multiple cameras and depth sensors in various setups and may be made up of thousands up to billions of points in order to realistically represent reconstructed scenes. Therefore, the compression technologies are needed to reduce the amount of data required to represent a point cloud. In this talk, we will give an introduction of concept of point cloud and requirements of point cloud compression, also we will review several existing compression schemes.