Guidance for assemblable parts is a promising field for augmented reality. Augmented reality assembly guidance requires 6D object poses of target objects in real time. Especially in time-critical medical or industrial settings, continuous and markerless tracking of individual parts is essential to visualize instructions superimposed on or next to the target object parts. In this regard, occlusions by the user's hand or other objects and the complexity of different assembly states complicate robust and real-time markerless multi-object tracking. To address this problem, we present Graph-based Object Tracking (GBOT), a novel graph-based single-view RGB-D tracking approach. The real-time markerless multi-object tracking is initialized via 6D pose estimation and updates the graph-based assembly poses. The tracking through various assembly states is achieved by our novel multi-state assembly graph. We update the multi-state assembly graph by utilizing the relative poses of the individual assembly parts. Linking the individual objects in this graph enables more robust object tracking during the assembly process. For evaluation, we introduce a synthetic dataset of publicly available and 3D printable assembly assets as a benchmark for future work. Quantitative experiments in synthetic data and further qualitative study in real test data show that GBOT can outperform existing work towards enabling context-aware augmented reality assembly guidance. Dataset and code will be made publically available.

翻译：可装配部件的引导是增强现实的一个具有前景的研究领域。增强现实装配引导需要实时获取目标物体的六自由度位姿。特别是在时间紧迫的医疗或工业场景中，持续且无标记地跟踪单个部件对于在目标部件上方或旁边可视化指令至关重要。然而，用户手部或其他物体的遮挡以及不同装配状态的复杂性，给鲁棒且实时的无标记多目标跟踪带来了挑战。为解决这一问题，我们提出基于图的物体跟踪方法（GBOT），一种新颖的基于单视角RGB-D的图跟踪方法。该方法通过六自由度位姿估计初始化实时无标记多目标跟踪，并更新基于图的装配位姿。通过我们提出的新型多状态装配图，实现了跨不同装配状态的跟踪。我们利用单个装配部件的相对位姿来更新多状态装配图。将图中各个物体关联起来，能够在装配过程中实现更鲁棒的物体跟踪。为进行评估，我们引入了一个由公开可获取及3D打印装配资产组成的合成数据集，作为未来工作的基准。合成数据的定量实验及真实测试数据的进一步定性研究表明，GBOT在实现上下文感知增强现实装配引导方面可超越现有工作。数据集和代码将公开发布。