摘要：Target selection is one of the most common and important tasks in interactive systems. Within virtual reality environments, target selection can pose extra challenges to users because targets can be located far away, clustered together, and occluded from view. Although selection techniques have been explored, it is often unclear which techniques perform better across different environmental target density levels and which have higher levels of usability especially for recently released commercial head-mounted display (HMD) virtual reality systems and input devices. In this paper, we first review previous studies on target selection in HMD VR environments. We then compare the performances of three main techniques or metaphors (RayCasting, Virtual Hand, and Hand-Extension) using recently marketed VR headsets and input devices under different density conditions and selection areas. After, we select the best two techniques (RayCasting and Virtual Hand) for the second experiment to explore their relative performance and usability by adding different feedback to these two techniques. In the third experiment, we implemented three techniques with pointing facilitators and compared them against the best techniques from the second experiment, RayCasting with visual feedback, to assess their performance, error rates, learning effects, and usability. The three studies altogether suggest the best target selection features, based on techniques, feedback, and pointing facilitators for target density conditions in HMD VR environments.
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