Minsik Jeon
Hello! I am a MS in Robotics student at Carnegie Mellon University (CMU), advised by Prof. Shubham Tulsiani. I am interested in 3D and 4D reconstruction from perceptual input, and its applications in robotics.
Prior to joining CMU, I served as a researcher at the Agency for Defense Development (ADD) in South Korea while concurrently completing my mandatory military service. I completed my bachelor's degree (Summa Cum Laude) in School of Computing at KAIST (Korea Advanced Institute of Science and Technology).
Latest News
Mar 2026
One paper accepted to CVPR 2026! Flow3r is a factored flow-prediction framework for scalable 3D/4D reconstruction, trained on ~800K unlabeled videos.
Jan 2026
One paper accepted to RA-L! E2-BKI is an uncertainty-aware Gaussian semantic mapping framework that combines Evidential Deep Learning with Bayesian Kernel Inference for outdoor robots.
Nov 2025
One paper accepted to WACV 2026! OWOD-Rep is an Open-World object detection method that leverages Vision Foundation Models for novel-object detection and semantically rich feature extraction.
Aug 2025
Started M.S. in Robotics at Carnegie Mellon University, advised by Prof. Shubham Tulsiani.
May 2024
Presented DA-RAW at ICRA 2024 in Yokohama, Japan.
Publications (* denotes equal contribution)
Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
A scalable framework for 3D/4D reconstruction that uses dense 2D correspondences from unlabeled videos as supervision. Factored flow prediction decomposes flow into geometry and pose components, enabling training on ~800K unlabeled videos.
Open-World Object Detection with Instance Representation Learning
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026
A method for Open-World object detection that improves novel object detection and semantically rich feature extraction by leveraging Vision Foundation Models.
E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
IEEE Robotics and Automation Letters (RA-L), 2026
An uncertainty-aware semantic mapping framework that combines Evidential Deep Learning with Bayesian Kernel Inference, capturing multiple sources of uncertainty for outdoor robot mapping while preserving real-time efficiency.
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions
IEEE International Conference on Robotics and Automation (ICRA), 2024
An UDA framework for object detection that can effectively adapt to real-world adverse weather conditions by addressing Style & Weather gaps separately.
Projects
Adaptive Path Planning Based on Situational Awareness and Dynamic Model Learning
Agency for Defense Development, 2023 – Present
Developing perception and control algorithms for off-road navigation: robust moving object detection & tracking in off-road environments, and semantic terrain classification maps via LiDAR–Camera integration. Examined path planning and control algorithms for navigation in complex environments.
Unmanned Reconnaissance Vehicles Development
Agency for Defense Development, 2023
Developed a LiDAR–IR fusion method for robust object detection under visibility-constrained scenarios.
Deformable Object Recognition Technology
Agency for Defense Development, 2022 – 2023
Developed an open-set 2D & 3D object detection method for LiDAR pointcloud.
Indy Autonomous Challenge (IAC)
USRG @ KAIST (Advised by Prof. David Hyunchul Shim), 2021
Participated in the Indy Autonomous Challenge (IAC), the first autonomous car racing competition to be held in Indianapolis, as an intern for Team KAIST. As part of the perception team, I tested algorithms using camera and LiDAR in a simulation environment.
Microscopy Image Artifact Removal & Super-resolution
NICA Lab @ KAIST (Advised by Prof. Young-Gyu Yoon), 2021
Developed a removal network that addresses the line artifact of SCAPE microscopy images, using a CycleGAN-like model and transferring the image into a CFM style.
Performance and Operation Analysis of On-Board RAID
SK Hynix, 2020
In the Performance Analysis team, configured On-board RAID using SSDs and conducted performance evaluations for each RAID option.