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

2026-06-06

AI Summary

This study addresses the lack of systematic evaluation of controller-free pointing techniques for selecting 2D targets at varying depths in augmented reality (AR). Following the ISO 9241-411 standard, it presents the first comprehensive comparison of five controller-free input modalities—head, eye, finger, wrist, and arm pointing—across interaction distances of 2, 6, and 10 meters, complemented by quantitative analysis using the NASA-TLX workload scale. Results demonstrate that head-based pointing significantly outperforms hand-based methods in both selection accuracy and cross-depth stability. Furthermore, depth variations interact significantly with target size and distance, profoundly influencing selection efficiency and cognitive workload. These findings provide empirical foundations and practical guidance for designing multi-depth AR interfaces.

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 (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.

Problem

• augmented reality
• controller-free pointing
• 2D selection
• depth variation
• pointing techniques

Innovation

• controller-free interaction
• augmented reality
• pointing techniques
• depth variation
• ISO 9241-411