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Learning-based 3D Point Cloud Enhancement: from Static to Dynamic
3D point clouds are widely used in immersive telepresence, cultural heritage reconstruction, geophysical information systems, autonomous driving, and virtual/augmented reality. Despite rapid development in 3D sensing technology, acquiring 3D point cloud data with high spatial and temporal resolution and complex geometry/topology is still time-consuming, challenging, or costly.
This talk will present our recent studies on computational methods (i.e., deep learning)-based 3D point cloud reconstruction, including sparse 3D point cloud upsampling, temporal interpolation of dynamic 3D point cloud sequences, and adversarial 3D point cloud generation.
讲师信息：
Junhui Hou (Senior Member) has been an Assistant Professor with the Department of Computer Science, City University of Hong Kong since Jan. 2017. He received the B.Eng. degree in information engineering (Talented Students Program) from the South China University of Technology, Guangzhou, China, in 2009, the M.Eng. degree in signal and information processing from Northwestern Polytechnical University, Xian, China, in 2012, and the Ph.D. degree in electrical and electronic engineering from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2016. His research interests fall into the general areas of multimedia signal processing, such as image/video/3D geometry data representation, processing and analysis, semi/un-supervised data modeling, and data compression.
⏰ 活动时间：2022.7.12 | 19：00
