Projects per year
Abstract
Exploded view animations are widely used for communication of complex mechanical assemblies in fields such as manufacturing and education. The creation of these animations is largely a manual process that requires expertise knowledge. In this paper, we introduce a novel tool for creating exploded views within a virtual reality (VR) environment, offering a human-in-the-loop process allowing users to adjust the final animation. Our approach combines principles from traditional assembly-by-disassembly with modern machine learning techniques to determine the order and direction of part disassembly. The core of our methodology is a novel point cloud classification network, PointDAN, for predicting the disassembly of parts. Another key contribution is the development of a public point cloud dataset, facilitating the training of models to predict whether parts can be disassembled. We report the performance of our trained networks, along with the performance of the full assembly-by-disassembly process. Furthermore, we report on an expert user study including participants spanning various industries. This study demonstrates the industrial applicability and potential of the tool. Our findings show that the integration of assembly-by-disassembly and machine learning not only simplifies the automatic generation of exploded layouts but has the potential to create highly accurate animations. A project page containing the dataset and code will be made available upon acceptance of the manuscript at https://github.com/jgaarsdal/ExplodedViewAnimationVR
Original language | English |
---|---|
Journal | I E E E Transactions on Visualization and Computer Graphics |
Number of pages | 11 |
ISSN | 1077-2626 |
Publication status | Submitted - 21 Dec 2023 |
Keywords
- Virtual Reality
- Animation
- Machine Learning
- Industrial Applications
- Dataset
Fingerprint
Dive into the research topics of 'Automatically Generated Exploded View Animations in VR: A Deep Learning Approach'. Together they form a unique fingerprint.Projects
- 1 Finished
-
AAIVR: Animation Authoring for Industrial VR Applications: Integrating Deep Learning and Motion Paths
Gaarsdal, J. (PI), Madsen, C. B. (Supervisor) & Wolff, S. (Supervisor)
01/02/2021 → 31/01/2024
Project: Research
-
Animation Authoring for Industrial VR Applications: Integrating Deep Learning and Motion Paths
Gaarsdal, J., 2024, Aalborg University Open Publishing. 180 p.Research output: PhD thesis
Open AccessFile48 Downloads (Pure) -
AssemblyNet: A Point Cloud Dataset and Benchmark for Predicting Part Directions in an Exploded Layout
Gaarsdal, J., Haurum, J. B., Wolff, S. & Madsen, C. B., Apr 2024, 2024 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE (Institute of Electrical and Electronics Engineers), p. 5824-5833 10 p. (IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Open Access -
Real-Time Exploded View Animation Authoring in VR Based on Simplified Assembly Sequence Planning
Gaarsdal, J., Wolff, S. & Madsen, C. B., 1 May 2023, Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023. IEEE (Institute of Electrical and Electronics Engineers), p. 667-668 2 p.Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Open AccessFile1 Citation (Scopus)29 Downloads (Pure)