Comparing Controller-Free Pointing Techniques Across Depth for 2D Selection in Augmented Reality

Abstract

This paper presents a systematic evaluation of five controller-free pointing techniques for 2D target selection in AR, using ISO 9241-411. We compared them across multiple depths (2,m, 6,m, 10,m) in terms of movement time, accuracy, throughput, and workload \mbox{(NASA TLX)}. Head- and eye-based pointing significantly outperformed the hand-based methods (Finger, Wrist, and Arm); Head input was the most accurate and remained the most consistent across depth. Depth significantly impacted performance, with complex interactions with target size and distance. Our results offer a comprehensive empirical basis for selecting appropriate controller-free techniques in depth-varying AR tasks.