GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
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.
Code (1)
Tasks
3D Object Tracking6D Pose EstimationMulti-Object TrackingObjectObject TrackingPose EstimationSimilar Papers 제목 키워드 기반
Instant 3D Object Tracking with Applications in Augmented Reality
Tracking object poses in 3D is a crucial building block for Augmented Reality applications. We propose an instant motion tracking system that tracks an object's pose in space (represented by its 3D bounding box) in real-…
3D Object TrackingCPUGPUObject TrackingDeep Learning-based Lightweight RGB Object Tracking for Augmented Reality Devices
Augmented Reality (AR) applications often require robust real-time tracking of objects in the user's environment to correctly overlay virtual content. Recent advances in computer vision have produced highly accurate deep…
Knowledge DistillationObject TrackingLearnable Online Graph Representations for 3D Multi-Object Tracking
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. Most recent approaches for 3D multi object trac…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject TrackingAugmented Assembly: Object Recognition and Hand Tracking for Adaptive Assembly Instructions in Augmented Reality
Recent advances in augmented reality (AR) have enabled interactive systems that assist users in physical assembly tasks. In this paper, we present an AR-assisted assembly workflow that leverages object recognition and ha…
Object RecognitionA Survey of Calibration Methods for Optical See-Through Head-Mounted Displays
Optical see-through head-mounted displays (OST HMDs) are a major output medium for Augmented Reality, which have seen significant growth in popularity and usage among the general public due to the growing release of cons…
Position