3D Object Selection is one of the fundamental tasks in virtual reality applications and the initial task for most common user's interactions in a virtual environment. We have analyzed major factors influencing selection performance, and proposed new techniques for facilitating selection in 3D space. Considering the frequency of selection tasks in a typical virtual reality workflow, improving selection tasks often results in significant gains in the overall user performance.

A 3D selection task requires the user to gesture in 3D space, e.g. grabbing an object or pointing to something. The success or failure of the task depends mainly on the interaction technique, the dexterity of the user, and the spatial perception of the virtual environment. Since the dexterity of the user can be improved by training, we focus on how to take advantage of existing human control models to minimize the effort required to select an object, and how to enhance the user's spatial perception of the virtual environment to facilitate selection and referral tasks. We propose several selection techniques based on Fitts’ Law and study how visual feedback can be used to overcome spatial perception limitations in virtual environments. The techniques proposed are not only oriented to achieve performance gains as we also account for user's preferences.

All the work done in this project was for my PhD, which I did at the MOVING research group at the Universitat Politècnica de Catalunya, my advisor was Carlos Andujar. The results achieved have been published in several journals and conferences, the complete list of publications can be found at the publications section.