Junsu Kim, 김준수
I am a MS student in 3D Vision & Robotics Lab, Department of Artificial Intelligence at UNIST, advised by Kyungdon Joo. During my MS, I worked as an intern at 42dot Inc. and at BIG (roBot Ingelligence Group) of Robot Institute at CMU, advised by Jean Oh. I received my BS from the Department of Automobile and IT Convergence at Kookmin University, advised by Sanghun Lee.
Research
🚀 My research interests lie in the fields of 3D computer vision and machine learning, particularly 3D vision for autonomous driving and robot arm, but not limited to.
VPOcc: Exploiting Vanishing Point for Monocular 3D Semantic Occupancy Prediction
Under review [ Paper ]
By utilizing a vanishing point in monocular 3D semantic occupancy prediction, this approach aims to enhance performance by incorporating camera perspective geometry into the network.
Monocular Fisheye 3D Object Detection
On-going
We propose a monocular 3D object detection model for fisheye cameras using the geometric concept of the fisheye camera.
Project
Enhancing Surround-view 3D Object Detection with Fisheye Camera
Junsu Kim
Building on a basic surround-view system of pinhole cameras, this approach incorporates fisheye cameras to enhance the performance of multi-view 3D object detection.
Exploring Point Cloud Attention: Unraveling its Impact on Point Cloud Shape Completion
Junsu Kim, Minje Kim, Dongjun Gu
Principles of Deep Learning Course Project, 2023
In a point cloud completion method using Transformers, we analyze attention tendency in the encoder and decoder, and based on this, propose a method using Minimum Density Sampling (MDS).
DeepPAVE: Deep learning-based Personalized Autonomous VehiclE
Team HAVI (Project leader)
The 9th Cloud ProgrammingWorld Cup, 2021 [ Poster ] [ Certification ]
Building a personalized autonomous driving system via socket communication between Virtual Simulation, Deep Learning Model, K7 Simulator, and GUI.
Deep-Learning-based Personalization of Driving Behavior of Autonomous Vehicles
Junsu Kim, Jeongsu Sun, Seungyoon Lee, Ahhyeon Lee, Juhee Lee, Sang Hun Lee
KSAE 2021 Annual autumn conference & exhibition [ Paper ] [ Poster ] [ Certification ] [ Link ]
Using simulations and ADAS, we developed a model for personalized autonomous driving by predicting lane change timing and trajectory from individual driving data.