Towards Subcentimeter Accuracy Digital-Twin Tracking via An RGBD-based Transformer Model and A Comprehensive Mobile Dataset

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.