报告题目：Point Cloud Compression and Communication
报 告 人：Zhu Li
主 持 人：张鼎文 教授
报告时间：2021年4月26日（周一）北京时间9点30分
报告地点：线上报告 腾讯会议 337 927 453
报告简介：Point cloud data arises from 3D sensing and capturing for both auto driving/navigation/smart city, as well as VR/AR playback and immersive visual communication applications. Recent advances in sensor technology and algorithms, especially LiDAR and 77Ghz mmWave radar systems, and very high resolution RGB camera arrays, have made point cloud capture getting closer to wide adoption in real world applications. In this talk I will overview the related research at the Multimedia Computing & Communication Lab at UMKC, discuss the main technical challenges and solutions in point cloud capture, compression and communication, especially the scalable geometry compression, MPEG video based point cloud compression (PCC) problems, and our recent results in advanced 3D motion model, occupancy map driven rate-distortion optimization and learning based point cloud QoE metrics that significantly advanced the state of art in video based PCC.
报告人简历：
Zhu Li is an associated professor with the Dept of CSEE, University of Missouri, Kansas City, USA, directs the NSF I/UCRC Center for Big Learning at UMKC. He received his PhD from Electrical & Computer Engineering from Northwestern University in 2004. He was AFOSR summer faculty fellow with the US Air Force Academy, 2016-18, 2020, Sr. Staff Researcher/Sr. Manager with Samsung Research America's Multimedia Core Standards Research Lab in Dallas, from 2012-2015, Sr. Staff Researcher at FutureWei, from 2010-12, Assistant Professor with the Dept of Computing, The HongKong Polytechnic University from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include image/video analysis, compression, and communication and associated optimization and machine learning problems. He has 46 issued or pending patents, 100+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He is the Associate Editor-in-Chief for IEEE Trans on Circuits & System for Video Tech, 2020~, and served and serving as Associated Editor for IEEE Trans on Image Processing (2019~), IEEE Trans on Multimedia (2015-18), and IEEE Trans on Circuits & System for Video Tech (2016~19). He received a Best Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and a Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007.
