Ph.D. candidate in EECS
I am a Ph.D. candidate in Electrical Engineering and Computer Science at GIST, advised by Prof. Hae-Gon Jeon. I received my B.S. in EECS from GIST in 2019. My research focuses on 3D scene reconstruction, currently centered on 4D Gaussian Splatting and neural rendering.
Gwangju Institute of Science and Technology
News
| Feb2025 | One paper on crowd behavior generation is accepted at CVPR 2025 and selected as Outstanding Reviewer! |
| Sep2024 | One paper on 4D Gaussian splatting is accepted at NeurIPS 2024 and selected as Top Reviewer! |
| Apr2024 | One paper on deviant place recognition is accepted at TPAMI! |
| Feb2024 | Two papers on language and depth model is accepted at CVPR 2024! |
| Jan2024 | One paper on unbounded NeRF is accepted at ICLR 2024! |
| Feb2022 | One paper on deviant place recognition is accepted at AAAI 2022! |
| Mar2020 | Starting my PhD program at Gwangju Institute of Science and Technology. |
Research Interests
Skills
Resume
Summary
Junoh Lee
- GIST, Electrical Engineering and Computer Science Building C, Room 412
123, Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea - juno@gm.gist.ac.kr
Research Experience
Research Intern
2017 - 2018
Gwangju Institute of Science and Technology, Gwangju, South Korea
- Researched natural language processing
Education
PhD of Electrical Engineering and Computer Science & Graphic Design
2020 - Present
Gwangju Institute of Science and Technology, Gwangju, South Korea
Advisor: Prof. Hae-Gon Jeon
Bachelor of Electrical Engineering and Computer Science & Mathematics
2015 - 2019
Gwangju Institute of Science and Technology, Gwangju, South Korea
Advisor: Prof. Jonghyun Choi
Publications
- All
- Conference
- Journal
- patent
Continuous Locomotive Crowd Behavior Generation
Computer Vision and Pattern Recognition (CVPR) 2025
Fully Explicit Dynamic Gaussian Splatting
Neural Information Processing Systems (NeurIPS) 2024
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction
Computer Vision and Pattern Recognition (CVPR) 2024
DevianceNet: Learning to Predict Deviance from A Large-scale Geo-tagged Dataset
The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) 2022
Confidence determining method of input depth map using layered depth map
KR Patent No. 10-2743469, Dec. 2024
Radiance field implementation method and system using adaptive mapping function
US Patent App. 19/002,445, Dec 2024
Trajectory prediction method and system using a large language model
US Patent App. 18/986,359, Dec 2024
Model learning method and system capable of sensor-agnostic depth map inference through depth prompting, and depth map inference method and system using the same
US Patent App. 19/002,363, Dec 2024
Radiance field implementation method and system using adaptive mapping function
KR Patent App. 10‑2024‑0044389, Apr 2024
Trajectory prediction method and system using a large language model
KR Patent App. 10-2024-0037756, Mar 2024
Model learning method and system capable of sensor-agnostic depth map inference through depth prompting, and depth map inference method and system using the same
KR Patent App. 10‑2024-0041415, Nar 2024
Contact
GIST, Electrical Engineering and Computer Science Building C, Room 412
123, Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea