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Robust Visual Tasks under Challenging Illumination Conditions
ABSTRACT：
Adverse lighting, blur, noise, and low resolution still pose serious challenges for visual systems in real-world applications like robotics and autonomous driving. This talk introduces a set of methods—from 2D to 3D vision, and from RGB to camera RAW pipelines—aimed at improving robustness under complex lighting. These techniques advance low-level vision and 3D modeling and support applications in autonomous driving, medical imaging, and surveillance.
BIOGRAPHY:
Ziteng Cui is an Assistant Professor at the University of Tokyo, where he received his Ph.D. in May 2025. His research focuses on computer vision, computational photography, and visual robustness, with an emphasis on camera RAW-based vision and 3D modeling in low-level vision. He has published over ten first-author papers at top conferences including CVPR, ICCV, ECCV, and AAAI, with 800+ citations on Google Scholar. He serves as a reviewer for major conferences and journals such as CVPR, ICCV, NeurIPS, and TPAMI, and was recognized as an Outstanding Reviewer at CVPR. His dissertation was selected for the CVPR 2025 Doctoral Consortium. He also collaborates with Sony AI (Japan), RIKEN AIP, and Shanghai AI Lab, gaining valuable industrial experience.
Date and Time
Location
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- No. 800, Dongchuan Road
- Shanghai, Shanghai
- China
- Building: Microelectronic Building
- Room Number: 306
- Contact Event Host
- Co-sponsored by Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University
- Starts 18 June 2025 08:00 AM UTC
- Ends 25 June 2025 02:00 AM UTC
- No Admission Charge