Hyunjoon Lee

I am an integrated MS/PhD student at VGI Lab (Visual and Geometric Intelligence Lab) at Seoul National University, where I am advised by Prof. Jaesik Park.

My research interests lie at the intersection of 3D vision and robotics. I am particularly interested in how we can leverage rich scene information to enhance robot manipulation capabilities in complex environments.

Education

Publications

* equal contribution

PairGS Project
Eunsung Cha*, Hyunjoon Lee*, Jaesik Park
Under Review, 2026

We propose a novel framework that reframes Gaussian segmentation as modeling pairwise relations between Gaussians, unifying instance-level coherence and query flexibility without per-scene optimization.

👀 See the robot demo!
MMVT Project
Jinmo Kim, Namtae Kim, Hyunjoon Lee, Seungha Kim, Jaesik Park
Under Review, 2026

We propose a feed-forward multi-view 3D reconstruction framework that explicitly reasons about occlusions by reconstructing missing latent tokens across views.

CF3 Project
Hyunjoon Lee, Joonkyu Min, Jaesik Park
ICCV (Main & Demonstrations Track), 2025

We propose a method to build compact and fast 3D Gaussian feature fields by effectively compressing and sparsifying Gaussians, achieving competitive performance with significantly fewer gaussians.

👀 See the Real World Demo!

⚡ Runs on a local laptop.


Domestic
Language-Driven Robotic Manipulation via Open-Vocabulary 3D Gaussian Scene Understanding
Hyunjoon Lee, Eunsung Cha, Jaesik Park
KCC (Outstanding Presentation Award), 2026
Aligning 2D Mask Space Consistency Using 3D Gaussian Splatting
Eunsung Cha, Hyunjoon Lee, Jaesik Park
KCC (Outstanding Presentation Award), 2026
Efficient Feature Lifting and Compression Using Pre-trained 3D Gaussians
Hyunjoon Lee, Joonkyu Min, Jaesik Park
KCC (Outstanding Presentation Award), 2025

Projects

Real2Sim VLA

A VLA model was fine-tuned on real-world scene data, and the corresponding scene was reconstructed with 2DGS and converted into a USD-compatible mesh for integration with Isaac Sim. This enables the VLA to run in a photorealistic Real2Sim environment for post-training and diverse scene-level evaluation.

Experience

Awards & Honors

Patents