Sangyun Shin

Sangyun Shin is a Researcher in the Department of Computer Science at the University of Oxford, supervised by Professors Niki Trigoni and Andrew Markham. His work focuses on advancing object localization systems for robotics through 3D vision technologies. He specializes in integrating multimodal sensing (e.g., RGB-D cameras, acoustic arrays) to enhance robotic perception in challenging environments.

His research interests include 3D motion capture for wildlife, domain-adaptive 3D detection, and self-supervised learning for nighttime vision. He has explored applications ranging from long-range wildlife tracking to autonomous drone navigation using reinforcement learning. Recent efforts emphasize sensor fusion and neural network architectures tailored for dynamic environments.

Key technical contributions include the SoundLoc3D system for 3D sound localization and the WildPose framework for wildlife motion capture. His publications span topics like acoustic neural warping fields (SPEAR), spherical point cloud segmentation, and LiDAR-based object detection for urban driving.

Shin's work bridges theoretical advancements in machine learning with practical robotics applications, addressing challenges such as cross-domain adaptation and low-resource sensor setups. His research has implications for autonomous systems, environmental monitoring, and human-drone interaction interfaces.