Title：Differentiable Projector-Camera Systems: Algorithm and Applications
Speaker：Professor Haibing Ling
State University of New York
Host：Professor Zhi Tang
Date & Time：2023/6/16 14:00 - 15:00
VooV Meeting：528-891-953
Abstract:
Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices and sampling images. In practice, this may be cumbersome for SAR applications to address them one-by-one. In this talk, I will introduce a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks. DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses: shading attributes estimation, rough direct light estimation and photorealistic neural rendering. DeProCams shows clear advantages over previous arts with promising quality and meanwhile being fully differentiable. Moreover, by solving the three tasks in a unified model, DeProCams waives the need for additional optical devices, radiometric calibrations and structured light patterns.
Biography:
Haibin Ling, received a bachelor's degree in 1997 and a master's degree in 2000 from Peking University. In 2006, he obtained his Ph.D. degree from the University of Maryland in the United States. From 2006 to 2007, he worked as a postdoctoral researcher at the University of California, Los Angeles (UCLA). From 2000 to 2001, he served as an assistant researcher at Microsoft Research Asia. From 2007 to 2008, he worked as a researcher at Siemens Research. From 2008 to 2019, he held a position at Temple University in the United States, and in 2019, he joined the Department of Computer Science at the State University of New York as a tenured professor.
His main research areas include computer vision, augmented reality, medical image analysis, and human-computer interaction. He received the ACM UIST Best Student Paper Award in 2003, the NSF CAREER Award in 2014, and the IEEE VR Best Journal Paper Award in 2019. He serves as an editor for journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Visualization and Computer Graphics (TVCG), Computer Vision and Image Understanding (CVIU), and Pattern Recognition. He has also served as the area chair for conferences such as CVPR, ICCV, ECCV, and others multiple times.
