The potential of digital twin technology, involving the creation of precise
digital replicas of physical objects, to reshape AR experiences in 3D object
tracking and localization scenarios is significant. However, enabling 3D object
tracking with subcentimeter accuracy in dynamic mobile AR environments remains
a formidable challenge. These scenarios often require a more robust pose
estimator capable of handling the inherent sensor-level measurement noise. In
this paper, recognizing the absence of comprehensive solutions in existing
literature, we build upon our previous work, the Digital Twin Tracking Dataset
(DTTD), to address these challenges in mobile AR settings. Specifically, we
propose a transformer-based 6DoF pose estimator designed to withstand the
challenges posed by noisy depth data. Simultaneously, we introduce a novel RGBD
dataset captured using a cutting-edge mobile sensor, the iPhone 14 Pro,
expanding the applicability of our approach to iPhone sensor data. Through
extensive experimentation and in-depth analysis, we illustrate the
effectiveness of our methods in the face of significant depth data errors,
surpassing the performance of existing baselines. Code will be made publicly
available.

数字孪生技术在移动增强现实环境中，通过创建精确的物理对象数字副本，对于重塑三维物体追踪和定位场景具有重要潜力。本文基于我们之前的工作《数字孪生追踪数据集》（DTTD），针对移动增强现实环境中的挑战，提出了一种基于转换器的 6 自由度姿态估计器，并引入了使用 iPhone 14 Pro 的创新 RGBD 数据集，通过大量实验和深入分析，证明了我们方法在处理深度数据错误时的有效性，超过现有基线的性能。代码将公开提供。