Ki‐Ryong Kwon

About

Ki-Ryong Kwon is a researcher at the forefront of computer vision and deep learning, with a specialized focus on 3D object analysis and visual tracking. His work bridges the gap between theoretical deep learning models and practical applications in autonomous systems, robotics, and augmented reality. Kwon’s major contributions include pioneering multi-modal deep learning networks for 3D point cloud classification, such as GSV-NET, which leverages LiDAR data for enhanced environmental perception. He has also developed novel methods for 3D object classification and retrieval using advanced signatures like the Global Point Signature Plus and Wave Kernel Signature, achieving robust performance on complex mesh data. In visual tracking, Kwon has applied deep reinforcement learning—specifically DQN agents—to improve object tracking in virtual simulations, addressing real-world challenges in indeterminable environments. With over 60 citations across his most-cited works, his research has been recognized for its impact on intelligent robotics and autonomous driving. Kwon’s innovative algorithms, including the integration of center-point analysis for 3D triangle meshes, continue to shape the future of computer vision and multimedia processing.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation

   30 citations · 2022
2. 2
   GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

   16 citations · 2022
3. 3
   A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network

   11 citations · 2021
4. 4
   A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

   7 citations · 2019

Key Collaborators

Contact & Links