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
ARPOV: Expanding Visualization of Object Detection in AR with Panoramic Mosaic Stitching
Erin McGowan, Ethan Brewer, Claudio Silva
Precise Workcell Sketching from Point Clouds Using an AR Toolbox
Krzysztof Zieliński, Bruce Blumberg, Mikkel Baun Kjærgaard
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance
Hongchao Shu, Mingxu Liu, Lalithkumar Seenivasan, Suxi Gu, Ping-Cheng Ku, Jonathan Knopf, Russell Taylor, Mathias Unberath
AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues
Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Bernd Bewer, Sophie Jentzsch, Tobias Hecking, J. Nathan Kutz
SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality
Hongjia Zhai, Xiyu Zhang, Boming Zhao, Hai Li, Yijia He, Zhaopeng Cui, Hujun Bao, Guofeng Zhang