报告题目：Empowering Deep Modeling of 3D Geometry Data: From Representation, Learning Process, to Loss Function
时间：12月6日（周五）下午3点
地点：校本部计算机楼308
个人简介 ：Dr Junhui Hou is an Associate Professor with the Department of Computer Science, City University of Hong Kong. His research interests include multi-dimensional visual computing, such as light field, hyperspectral, geometry, and event data. He received the Early Career Award from the Hong Kong Research Grants Council in 2018 and the Excellent Young Scientists Fund from NSFC in 2024. He has served or is serving as an Associate Editor for IEEE TIP, TVCG, TMM, and TCSVT.
报告摘要：3D geometric data are becoming increasingly popular in various emerging applications, such as meta-verse, autonomous driving, and computer animations/games, as it provides an explicit representation of the geometric structures of objects and scenes. While deep learning has achieved great success in 2D image and video processing, designing efficient yet effective deep architectures and loss functions for 3D point cloud data is difficult, and as a result, the representation capability of existing deep architectures is limited. In this presentation, I will showcase our endeavors to push the boundaries of this field, starting with the fundamental representation, the development of a cross-modal learning mechanism, to the efficient yet effective loss function. These new perspectives are poised to unlock numerous possibilities in deep 3D point cloud data modeling.
