Augmented Reality
Augmented reality (AR) overlays digital information onto the real world, aiming to enhance user interaction and understanding of their environment. Current research focuses on improving the accuracy and robustness of AR systems, particularly in areas like 3D object recognition and pose estimation, often employing deep learning models (e.g., convolutional neural networks) and techniques such as simultaneous localization and mapping (SLAM) and 3D Gaussian splatting. These advancements are driving significant improvements in applications ranging from surgery and robotics to industrial automation and consumer experiences, enabling more precise and intuitive interactions with both physical and virtual worlds.
Papers
XAIR: A Framework of Explainable AI in Augmented Reality
Xuhai Xu, Mengjie Yu, Tanya R. Jonker, Kashyap Todi, Feiyu Lu, Xun Qian, João Marcelo Evangelista Belo, Tianyi Wang, Michelle Li, Aran Mun, Te-Yen Wu, Junxiao Shen, Ting Zhang, Narine Kokhlikyan, Fulton Wang, Paul Sorenson, Sophie Kahyun Kim, Hrvoje Benko
Cross-View Visual Geo-Localization for Outdoor Augmented Reality
Niluthpol Chowdhury Mithun, Kshitij Minhas, Han-Pang Chiu, Taragay Oskiper, Mikhail Sizintsev, Supun Samarasekera, Rakesh Kumar