Integrating User Input in Automated Object Placement for Augmented Reality.

Abstract

Object placement in Augmented Reality (AR) is crucial for creating immersive and functional experiences. However, a critical research gap exists in combining user input with efficient automated placement, particularly in understanding spatial relationships and optimal placement. This study addresses this gap by presenting a novel object placement pipeline for AR applications that balances automation with user-directed placement. The pipeline employs entity recognition, object detection, depth estimation along with spawn area allocation to create a placement system. We compared our proposed method against manual placement in a comprehensive evaluation involving 50 participants. The evaluation included user experience questionnaires, a comparative study of task performance, and post-task interviews. Results indicate that our pipeline significantly reduces task completion time while maintaining comparable accuracy to manual placement. The UEQ-S and TENS scores revealed high user satisfaction. While manual placement offered more direct control, our method provided a more streamlined, efficient experience. This study contributes to the field of object placement in AR by demonstrating the potential of automated systems to enhance user experience and task efficiency.