Beyond Task Performance: Lessons Learned from Evaluating an Exploratory VR Interaction Technique

2026-09-21

AI Summary

研究通过对比实验评估了RodCast技术在密集虚拟环境中对象选择和操作的表现，强调了控制简单性和定性反馈的重要性。

Abstract

Dense virtual environments present significant challenges for object selection and manipulation, motivating the development of novel interaction techniques. This paper presents RodCast as an exploratory case study to investigate explicit trajectory visualization for interaction in dense virtual environments. We conducted a within-subjects user study comparing RodCast with Go-Go Hand and FlowerCone across three representative interaction tasks using objective performance measures, subjective evaluations, and qualitative feedback. The results revealed that the proposed implementation incurred performance costs on more demanding manipulation tasks, while qualitative feedback suggested that participants attributed these challenges to the complexity of the bimanual control scheme. Additionally, subjective evaluations and qualitative feedback identified benefits in spatial awareness and target accessibility that were not fully reflected by conventional performance measures. Together, these findings highlight the importance of control simplicity in interaction technique design and emphasize that incorporating qualitative feedback into the evaluation process is essential for distinguishing implementation limitations from the potential of the interaction concept. Collectively, these lessons provide guidance for the design and evaluation of future interaction techniques.

Problem

• virtual environments
• object selection
• manipulation
• interaction techniques
• trajectory visualization

Innovation

• Explicit Trajectory Visualization
• Dense Virtual Environments
• Bimanual Control Scheme
• Spatial Awareness