Augmented Reality (AR) see-through vision is an interesting research topic since it enables users to see through a wall and see the occluded objects. Most existing research focuses on the visual effects of see-through vision, while the interaction method is less studied. However, we argue that using common interaction modalities, e.g., midair click and speech, may not be the optimal way to control see-through vision. This is because when we want to see through something, it is physically related to our gaze depth/vergence and thus should be naturally controlled by the eyes. Following this idea, this paper proposes a novel gaze-vergence-controlled (GVC) see-through vision technique in AR. Since gaze depth is needed, we build a gaze tracking module with two infrared cameras and the corresponding algorithm and assemble it into the Microsoft HoloLens 2 to achieve gaze depth estimation. We then propose two different GVC modes for see-through vision to fit different scenarios. Extensive experimental results demonstrate that our gaze depth estimation is efficient and accurate. By comparing with conventional interaction modalities, our GVC techniques are also shown to be superior in terms of efficiency and more preferred by users. Finally, we present four example applications of gaze-vergence-controlled see-through vision.
翻译:提高真实度(AR)透视是令人感兴趣的研究课题,因为它使用户能够通过墙看到并看到隐蔽的物体。大多数现有研究侧重于透视的视觉效果,而互动方法则较少研究。然而,我们认为,使用共同的互动模式,例如中空点击和语音,可能不是控制透视的最佳方法。这是因为当我们想通过某点看到时,它与我们的视觉深度/视觉有物理联系,因此应该自然地由眼睛控制。根据这个想法,本文件提出了一种新的视觉-视觉控制(GVC)透视技术。由于需要透视深度,我们用两个红外摄像头和相应的算法建立一个凝视跟踪模块,并将其汇集到微软霍洛伦斯2号,以达到透视深度估计。我们然后提出两种不同的GVC透视模式,以适应不同的情景。广泛的实验结果表明,我们的视觉深度估计是有效和准确的。与常规互动模式相比,我们GVC的视觉控制(GVC)控制(GVC)的视觉技术也显示在效率和视觉应用方面有更高的优势。