论文标题
面部探测的自我运动对齐，以进行协作增强现实
Ego-Motion Alignment from Face Detections for Collaborative Augmented Reality
论文作者
论文摘要
在多个智能眼镜佩戴者中共享虚拟内容是无缝协作增强现实体验的重要特征。为了启用共享，在每组眼镜上独立运行的基础6D自我置跟踪器的局部坐标系统必须在空间和时间上相互对齐。在本文中，我们为此问题提出了一种新颖的轻量级解决方案，该解决方案称为自我运动对准。我们表明，将彼此的脸或眼镜与跟踪器自我置于足够的情况下，可以使问题与空间联系在一起。重要的是，检测到的眼镜可以用作可靠的锚，以提供足够的准确性来实现实际使用。提出的想法使我们能够以基准标记或场景点为锚点放弃传统的视觉定位步骤。一种新型的封闭形式的最小求解器，该求解器解决了一个二次特征值问题，并引入了高斯信念传播的改进。实验验证了提出的方法并显示其高实用潜力。
Sharing virtual content among multiple smart glasses wearers is an essential feature of a seamless Collaborative Augmented Reality experience. To enable the sharing, local coordinate systems of the underlying 6D ego-pose trackers, running independently on each set of glasses, have to be spatially and temporally aligned with respect to each other. In this paper, we propose a novel lightweight solution for this problem, which is referred as ego-motion alignment. We show that detecting each other's face or glasses together with tracker ego-poses sufficiently conditions the problem to spatially relate local coordinate systems. Importantly, the detected glasses can serve as reliable anchors to bring sufficient accuracy for the targeted practical use. The proposed idea allows us to abandon the traditional visual localization step with fiducial markers or scene points as anchors. A novel closed form minimal solver which solves a Quadratic Eigenvalue Problem is derived and its refinement with Gaussian Belief Propagation is introduced. Experiments validate the presented approach and show its high practical potential.
