TY - GEN
T1 - GBOT
T2 - 31st IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2024
AU - Li, Shiyu
AU - Schieber, Hannah
AU - Corell, Niklas
AU - Egger, Bernhard
AU - Kreimeier, Julian
AU - Roth, Daniel
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.∗∗∗∗https://github.com/roth-hex-lab/gbot
AB - 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.∗∗∗∗https://github.com/roth-hex-lab/gbot
KW - Artificial intelligence
KW - Computer graphics
KW - Computer vision Computing methodologies
KW - Computing methodologies
KW - Graphics systems and interfaces
KW - Mixed / augmented reality
UR - http://www.scopus.com/inward/record.url?scp=85191532078&partnerID=8YFLogxK
U2 - 10.1109/VR58804.2024.00072
DO - 10.1109/VR58804.2024.00072
M3 - Conference contribution
AN - SCOPUS:85191532078
T3 - Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2024
SP - 513
EP - 523
BT - Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 March 2024 through 21 March 2024
ER -