Calibrated Passability Perception in Virtual Reality Transfers to Augmented Reality

Calibrated Passability Perception in Virtual Reality Transfers to Augmented Reality
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虚拟现实中校准的通行感知转移到增强现实

DOI:
10.1145/3613450
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发表时间:
2023
影响因子:
1.6
通讯作者:
Bodenheimer, Bobby
Bodenheimer, Bobby
中科院分区:
计算机科学4区
文献类型:
--
作者:
Gagnon, Holly;Stefanucci, Jeanine;Creem-Regehr, Sarah;Bodenheimer, Bobby

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随着虚拟现实(VR)和增强现实(AR)技术应用的增加,了解用户如何感知他们在虚拟环境中的动作能力将非常重要。关于动作的反馈可能有助于校准对动作机会(启示)的感知,使得VR和AR中的动作判断反映了演员的真实的能力。先前的工作表明,在挥舞物体的同时穿过虚拟门口可以通过碰撞的反馈来校准对一个人的可通过性的感知。在目前的研究中,我们的目标是通过在VR中使用不同范例的反馈来复制这种校准,同时还测试这种校准是否转移到AR。参与者以45°的角度握住一根杆子,并在AR(预测试阶段)中进行可通过性判断。然后,他们在VR中进行了可通过性判断,并通过手持杆子走过虚拟门口(校准阶段)来获得这些判断的反馈。然后,参与者返回AR进行测试后的可通过性判断。结果表明,反馈校准参与者在VR的判断。此外,这种校准转移到AR环境中。换句话说,在VR中体验反馈后,VR和AR中的可通过性判断变得更接近演员的实际能力,这可以使这些技术的培训应用更加有效。
As applications for virtual reality (VR) and augmented reality (AR) technology increase, it will be important to understand how users perceive their action capabilities in virtual environments. Feedback about actions may help to calibrate perception for action opportunities (affordances) so that action judgments in VR and AR mirror actors’ real abilities. Previous work indicates that walking through a virtual doorway while wielding an object can calibrate the perception of one’s passability through feedback from collisions. In the current study, we aimed to replicate this calibration through feedback using a different paradigm in VR while also testing whether this calibration transfers to AR. Participants held a pole at 45°and made passability judgments in AR (pretest phase). Then, they made passability judgments in VR and received feedback on those judgments by walking through a virtual doorway while holding the pole (calibration phase). Participants then returned to AR to make posttest passability judgments. Results indicate that feedback calibrated participants’ judgments in VR. Moreover, this calibration transferred to the AR environment. In other words, after experiencing feedback in VR, passability judgments in VR and in AR became closer to an actor’s actual ability, which could make training applications in these technologies more effective.
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