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
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System For Hyperspectral Classification Mapping with Depth Information for In-Vivo Surgical Procedures
Jaime Sancho, Manuel Villa, Miguel Chavarrías, Eduardo Juarez, Alfonso Lagares, César Sanz
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation
Hannah Schieber, Shiyu Li, Niklas Corell, Philipp Beckerle, Julian Kreimeier, Daniel Roth
Augmented Reality Warnings in Roadway Work Zones: Evaluating the Effect of Modality on Worker Reaction Times
Sepehr Sabeti, Fatemeh Banani Ardecani, Omidreza Shoghli
Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks
Aqeel Anwar, Tae Eun Choe, Zian Wang, Sanja Fidler, Minwoo Park