Shao, T, Li, D, Rong, Y et al. (2 more authors) (2016) Dynamic Furniture Modeling Through Assembly Instructions. ACM Transactions on Graphics, 35 (6). ARTN 172. ISSN 0730-0301
Abstract
We present a technique for parsing widely used furniture assembly instructions, and reconstructing the 3D models of furniture components and their dynamic assembly process. Our technique takes as input a multi-step assembly instruction in a vector graphic format and starts to group the vector graphic primitives into semantic elements representing individual furniture parts, mechanical connectors (e.g., screws, bolts and hinges), arrows, visual highlights, and numbers. To reconstruct the dynamic assembly process depicted over multiple steps, our system identifies previously built 3D furniture components when parsing a new step, and uses them to address the challenge of occlusions while generating new 3D components incrementally. With a wide range of examples covering a variety of furniture types, we demonstrate the use of our system to animate the 3D furniture assembly process and, beyond that, the semantic-aware furniture editing as well as the fabrication of personalized furnitures.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Graphics VOL 35, ISS 6, November 2016. : http://dx.doi.org/10.1145/2980179.2982416 |
Keywords: | Assembly instructions; furniture modeling; supervised learning; personalized fabrication |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Aug 2018 10:43 |
Last Modified: | 08 Aug 2018 03:34 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/2980179.2982416 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134260 |