Postdoctoral Fellow · Visual Intelligence Group, KIST
Fusing explicit geometry with learned models for metric, reliable 3D — point clouds → image-grounded 3D.
SFD-Net — an architecture-agnostic sharp-feature descriptor for point clouds — accepted to ECCV 2026. Project page · Paper details
Research
I'm a Postdoctoral Fellow in the Visual Intelligence Group, Center for AI Research, AI & Robotics Institute at the Korea Institute of Science and Technology (KIST). I received my Ph.D. in Mechanical and Robotics Engineering from GIST, advised by Prof. Kwang Hee Ko in the MODSIM Lab.
Reliable spatial perception needs two things at once: recognizing what is where, and pinning down where exactly, in metric 3D. Learned and foundation models handle the recognition well but stay geometrically loose, blurring the boundaries and discontinuities where 3D structure lives and losing absolute scale. I build the geometric half of this picture and inject it into learned and semantic models, so the recovered 3D becomes metric and structurally consistent.
The constant across my work is not a tool but a principle: identify the geometric relation a representation gets wrong, then supply the explicit cue the problem demands. For one problem that cue is a surface-normal field, for another a sharp-feature descriptor, for another a ground plane. Each project surfaced its own geometric bottleneck, and I built the module that resolved it.
On point clouds I established this geometric half: prior-free structure recovery, normal-guided LiDAR segmentation, noise-robust normal estimation, and SFD-Net, an architecture-agnostic sharp-feature descriptor that transfers across backbones and from synthetic CAD to real scans. I then carried the same scene-intrinsic geometry into images, first making real-time 6-DoF object pose from a single RGB image robust enough to deploy, and now, at KIST, recovering metric 3D from a moving camera where learned depth and multi-view reconstruction each fall short.
I am extending this toward image-grounded, semantic foundation models: supplying the geometric half they lack so their rich but up-to-scale output becomes metric, consistent, and structured. My aim is spatial perception in which explicit geometry is a first-class inductive bias, fused with learned recognition rather than left for it to infer.
Inyoung Oh is a postdoctoral fellow in the Visual Intelligence Group at the Korea Institute of Science and Technology (KIST). Oh received a Ph.D. in Mechanical and Robotics Engineering from the Gwangju Institute of Science and Technology (GIST) in 2026, advised by Prof. Kwang Hee Ko. Oh's research fuses explicit geometry — surface normals, sharp features, and ground planes — with learned and semantic models so that recovered 3D becomes metric, consistent, and structured, spanning point-cloud analysis (including SFD-Net, ECCV 2026) and metric 3D from monocular video.
News
Experience
Established the geometric half of this fusion: explicit scene-intrinsic cues, injected into learned models, that transfer across sensors, sampling density, and noise.
Sharp features & geometric descriptors
Normal estimation & segmentation
Pose, MR systems & primitive recognition
Education
GIST · Advisor: Prof. Kwang Hee Ko · GPA 4.2/4.5
Dissertation: Normal Vector Estimation, and Semantic Segmentation of 3D Point Clouds using Deep Learning and Geometric Analysis
GIST · Advisor: Prof. Kwang Hee Ko · GPA 3.91/4.5
Thesis: Sphere and Cylinder Detection in Kinect Point Clouds using RANSAC and the 2D Hough Transform
Chungnam National University · GPA 3.91/4.5
Thesis: Research on Semi-Automatic Drills — Grand Prize, Capstone Design Fair
Recognition
| Best Poster Award ×6, Korean Society for Computational Design and Engineering (CDE) | 2020 — 2025 |
| CDE DX Encouragement Award, Korean CDE Society | 2022 |
| Outstanding Ph.D. Student RA Scholarship ×5, GIST | 2018 — 2025 |
| GIST Full Scholarship (government-funded, M.S. & Ph.D.) | 2016 — 2026 |
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