Junho Kim | 김준호

I am a Research Intern at RideFlux, where I work on autonomous driving. I received my B.S. in Electrical Engineering from Kookmin University, where I worked with Prof. Seongwon Lee on 3D/4D scene understanding. Previously, I was a Research Intern at CARIAD, advised by Dr. Xavier Timoneda.

I am applying to PhD programs for Fall 2027.

Email  /  Google Scholar  /  Github  /  CV

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Research

My long-term goal is to build spatially grounded world models for autonomous agents that can predict how the physical world may evolve under possible actions and make reliable decisions.

I believe that understanding the world is most useful when it can inform an agent’s behavior. An autonomous agent does not need to model every aspect of its environment. It must capture the geometry, uncertainty, and dynamics that matter for the decisions it will eventually make.

My work so far has explored this question through 3D/4D scene understanding, and I am increasingly interested in extending it toward predictive models that reason about the consequences of actions. Ultimately, I want to understand how perception and world modeling can be learned together with the decisions they are meant to support.

Feed-Forward Refinement Selective Appearance Refinement for Feed-Forward 3D Gaussian Splatting
A plug-and-play module that selectively corrects low-confidence appearance predictions.
Junho Kim, Jiseok Kim, Seongwon Lee
International Conference on Control, Automation, and Systems (ICCAS), 2026
arXiv

RayOcc project image RayOcc: Occlusion-Aware Ray Occupancy Estimation via Gaussian Mixture Intensity
Multi-surface Ray occupancy modeling with Gaussian mixture model.
Junho Kim, Seongwon Lee
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026
arXiv

VG3T: Visual Geometry Grounded Gaussian Transformer
Cross-view geometry grounding produces cleaner 3D occupancy with fewer Gaussians.
Junho Kim, Seongwon Lee
IEEE International Conference on Robotics and Automation (ICRA), 2026
project page / arXiv / code

Project
High-Fidelity Vehicle Representation High-Fidelity Vehicle Representation via 3D Gaussian Splatting
Reflective vehicle reconstruction with disentangled diffuse and specular appearance.
Junho Kim, Sangho Byeon, Jiseok Kim, Seongwon Lee
Team Lead of We-Meet Project (Fall 2025)

Bosch Future Mobility Challenge 2024 Bosch Future Mobility Challenge 2024
Real-time autonomous driving perception and deployment on an embedded platform.
Minchan Jeong, Junho Kim, Dahyeon Ko, Jaehyoung Park, Donghwan Seo
Perception Engineer / ROS, Multi-View 3D object detection, V2X
3rd Prize, (top 3 out of 80 teams worldwide)

Experience
RideFlux, Seoul, South Korea
2026.08 - Present

Perception Research Intern
Kookmin University, Seoul, South Korea
2019.03 - 2026.08

B.S. in Electrical Engineering
Advisor: Prof. Seongwon Lee
CARIAD SE, Ingolstadt, Germany
2024.07 - 2024.12

Sensor Fusion Research Intern
Advisor: Dr. Xavier Timoneda
SEA:ME, Wolfsburg, Germany
2023.07 - 2024.06

Out-Bound Program
Advisor: Prof. Jongchan Kim
Republic of Korea Army, Gyeonggi-Do, South Korea
2020.12 - 2022.6

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Last updated: Aug. 6, 2026