GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 66 references that are in the map. First posted on arXiv 12 Feb 2024.
Builds on
30 of 66 references.
References · 30
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18 more
- 2023IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Settingcited by 46
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Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 3.
7 papers.
- 2025MoEdit: On Learning Quantity Perception for Multi-object Image Editingcited by 7
- 2024ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimationcited by 6
- 2025Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidancecited by 1
- 2026MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objectscited by 1
- 2025PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoningcited by 0
- 2026Towards an End-to-End System for 3D Tracking of Physical Objects in Virtual Immersive Environmentscited by 0
- 2026Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiencycited by 0
- 2026Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiency3 Jun 2026
- 2026Towards an End-to-End System for 3D Tracking of Physical Objects in Virtual Immersive Environments30 Mar 2026
- 2026MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects24 Mar 2026
- 2025PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning11 Dec 2025
- 2025MoEdit: On Learning Quantity Perception for Multi-object Image Editing13 Mar 2025
- 2025Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance17 Feb 2025
- 2024ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation25 Mar 2024