講座主題：Neural Spatial Computing: Geometric Modeling and Representation Learning
專(zhuān)家姓名：Junhui Hou
工作單位：香港城市大學(xué)
講座時(shí)間：2026年04月01日 15:30-16:30
講座地點(diǎn)：科技館4306
主辦單位：煙臺(tái)大學(xué)計(jì)算機(jī)與控制工程學(xué)院
內(nèi)容摘要：
Neural spatial computing, the ability to perceive, reason about, and manipulate spatial data (e.g., geometry) through neural network-based algorithms, is fundamental to advancing Al applications in robotics, AR/VR, and autonomous systems. In this talk, I will showcase our endeavors to push the boundaries of this field, starting with the fundamental representation and modeling of 3D scenes, to the development of a cross-modal/structural learning mechanism. These new perspectives are poised to unlock numerous possibilities.
主講人介紹：
Junhui Hou is a Professor with the Department of Computer Science, City University of Hong Kong. His research interests include multidimensional 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, IEEE Multimedia Rising Star Award in 2023, the Excellent Young Scientists Fund from NSFC in 2024, and the IEEE TIP Best Paper Award in 2025. He is serving as a Senior Area Editor for IEEE TIP and an Associate Editor for IEEE TVCG and TMM. He served as an Associate Editor for IEEE TIP and TCSVT.
