Gain A New Perspective: Towards Exploring Multi-View Alignment in Mixed Reality

Manufacturing, maintenance, assembly, and training tasks represent some of the human activities that have captured special interest for Mixed Reality (MR) applications. For most of these scenarios, accurate object alignment constitutes a requirement to ensure the desired outcome. This task has proved to be especially challenging in egocentric approaches, frequently leading to estimation errors in depth. While traditional MR methods provide virtual guides such as text, arrows, or animations to assist users during alignment, this work explores the feasibility of using additional views generated by virtual cameras and mirrors. Presenting additional views from different perspectives can help to mitigate the estimation errors and show information that is not directly visible to users. To explore the benefits of using additional views for alignment tasks, and to collect reliable data and diminish external factors, we conducted a user study in a controlled virtual environment where participants aligned objects supported by additional views from a top-down camera and virtual mirrors. Data regarding alignment error, time to completion, user’s interaction and attention, distance traveled, average head velocity, usability, and mental effort were collected. Our results show that using additional views reduces the mental effort and distance traveled by users and increases acceptance without negatively affecting the alignment accuracy. Therefore, we believe that users will also benefit from integrating these techniques during alignment tasks in MR environments.     «

Manufacturing, maintenance, assembly, and training tasks represent some of the human activities that have captured special interest for Mixed Reality (MR) applications. For most of these scenarios, accurate object alignment constitutes a requirement to ensure the desired outcome. This task has proved to be especially challenging in egocentric approaches, frequently leading to estimation errors in depth. While traditional MR methods provide virtual guides such as text, arrows, or animations to as...     »