Interpreting 2D gesture annotations in 3D augmented reality
Interpreting 2D gesture annotations in 3D augmented reality
复制标题
解释 3D 增强现实中的 2D 手势注释
DOI：
10.1109/3dui.2016.7460046
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Turk
中科院分区：
文献类型：
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作者：
B. Nuernberger;Kuo;Tobias Höllerer;M. Turk
2D手势注释提供了一种简单的方法来注释增强现实中的物理世界，用于远程协作等一系列应用程序。当从新的视点渲染时，这些注释以前只适用于静态定位的相机或平面场景。然而，如果摄像机移动并观察任意环境，2D手势注释很容易因透视效果而从新视点显示时失去其意义。在本文中，我们提出了一种新的方法来解决这个问题，即使用手势增强注释解释。通过首先对用户绘制的手势类型进行分类，我们表明可以以一种比传统方法更符合用户初衷的方式在3D中呈现2D注释。我们首先通过对88名参与者进行亚马逊土耳其机器人研究，确定了用于增强现实增强远程协作场景的重要2D手势的通用词汇表。接下来，我们设计了一种新颖的实时方法来自动处理两种最常见的2D手势注释-箭头和圆圈-并详细分析了每种情况下必须处理的歧义。箭头手势通过识别它们的锚点和使用场景表面法线来解释，以获得更好的透视渲染。对于圆形手势，我们设计了一个新的能量函数来帮助使用二维图像线索和三维几何线索推断感兴趣的对象。结果表明，我们的方法优于以往的方法，可以更好地从不同的角度传达原图的含义。
A 2D gesture annotation provides a simple way to annotate the physical world in augmented reality for a range of applications such as remote collaboration. When rendered from novel viewpoints, these annotations have previously only worked with statically positioned cameras or planar scenes. However, if the camera moves and is observing an arbitrary environment, 2D gesture annotations can easily lose their meaning when shown from novel viewpoints due to perspective effects. In this paper, we present a new approach towards solving this problem by using a gesture enhanced annotation interpretation. By first classifying which type of gesture the user drew, we show that it is possible to render the 2D annotations in 3D in a way that conforms more to the original intention of the user than with traditional methods. We first determined a generic vocabulary of important 2D gestures for an augmented reality enhanced remote collaboration scenario by running an Amazon Mechanical Turk study with 88 participants. Next, we designed a novel real-time method to automatically handle the two most common 2D gesture annotations - arrows and circles - and give a detailed analysis of the ambiguities that must be handled in each case. Arrow gestures are interpreted by identifying their anchor points and using scene surface normals for better perspective rendering. For circle gestures, we designed a novel energy function to help infer the object of interest using both 2D image cues and 3D geometric cues. Results indicate that our method outperforms previous approaches by better conveying the meaning of the original drawing from different viewpoints.