I am a Senior Research Engineer at Hyundai Motor Group.
Previously, I obtained my Ph.D and M.S degrees in Electrical Engineering from Hanyang University (HYU), where I was advised by Prof. Jun Won Choi. I also earned my B.S. degree in Electrical Engineering from Hanyang University. During my Ph.D. studies, I was a recipent of Hyundai Motor Group Research Scholarship and Huawei ICT Talent Development Scholarship.
My research interests lie in 3D perception and end-to-end learning for autonomous vehicles and mobile robots. In particular, I have conducted studies to advance 3D perception using various sensor setups, including multi-view cameras, LiDAR, and radar, as well as sensor fusion and temporal modeling. Based on these studies, I am developing practical and real-world deployable solutions for end-to-end autonomous driving systems.
junhkoh[at]hyundai.com
bononobo7[at]gmail.com
131, Bundangnaegok-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
PhD in Electrical Engineering, 2024
Hanyang University, Seoul
MS in Electrical Engineering, 2020
Hanyang University, Seoul
BS in Electrical Engineering, 2018
Hanyang University, Seoul
[Updated!!] OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
accepted to IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2025.
[ Paper ]
[Updated!!] RCTDistill: Cross-Modal Knowledge Distillation Framework for Radar-Camera 3D Object Detection with Temporal Fusion
International Conference on Computer Vision (ICCV), 2025.
[ Coming soon... ]
[Updated!!] Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition
accepted to Neurocomputing, 2025.
[ Paper ]
Fine-grained pillar feature encoding via spatio-temporal virtual grid for 3D object detection
IEEE International Conference on Robotics and Automation (ICRA), 2024.
[ Paper ]
MGTANet: Encoding sequential LiDAR points using long short-term motion-guided temporal attention for 3D object detection
Association for the Advancement of Artificial Intelligence (AAAI), 2023 (Acceptance: 1,721/8,777 < 20%) [Oral]
Ranked 3rd place among LiDAR methods on nuScenes object detection benchmark as of August 2022
[ Paper ]
D-Align: Dual query co-attention network for 3D object detection based on multi-frame point cloud sequence
IEEE International Conference on Robotics and Automation (ICRA), 2023.
[ Paper ]
Joint 3D object detection and tracking using spatio-temporal representation of camera image and LiDAR point clouds
Association for the Advancement of Artificial Intelligence (AAAI), 2022 (Acceptance: 1,349/9,251 < 15%)
[ Paper ]
Joint representation of temporal image sequences and object motion for video object detection
IEEE International Conference on Robotics and Automation (ICRA), 2021 [Oral]
[ Paper ]
Video object detection using object's motion context and spatio-temporal feature aggregation
IEEE International Conference on Pattern Recognition (ICPR), 2020
[ Paper ]
Enhanced object detection in bird's eye view using 3D global context inferred from Lidar point data
IEEE Intelligent Vehicles Symposium (IV), 2019
[ Paper ]
Robust deep multi-modal learning based on gated information fusion network
Asian Conference on Computer Vision (ACCV), 2018
[ Paper ]
Robust camera lidar sensor fusion via deep gated fusion network
IEEE Intelligent Vehicles Symposium (IV), 2018
Among 5% selected as single track oral presentation
[ Paper ]