LineUp : computing chain-based physical transformation
In this article, we introduce a novel method that can generate a sequence of physical transformations between 3D models with different shape and topology. Feasible transformations are realized on a chain structure with connected components that are 3D printed. Collision-free motions are computed to...
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sg-ntu-dr.10356-1447522020-11-23T06:45:17Z LineUp : computing chain-based physical transformation Yu, Minjing Ye, Zipeng Liu, Yong-Jin He, Ying Wang, Charlie Chang Ling School of Computer Science and Engineering Engineering::Computer science and engineering Computing Methodologies Computing Graphics In this article, we introduce a novel method that can generate a sequence of physical transformations between 3D models with different shape and topology. Feasible transformations are realized on a chain structure with connected components that are 3D printed. Collision-free motions are computed to transform between different configurations of the 3D printed chain structure. To realize the transformation between different 3D models, we first voxelize these input models into a similar number of voxels. The challenging part of our approach is to generate a simple path—as a chain configuration to connect most voxels. A layer-based algorithm is developed with theoretical guarantee of the existence and the path length. We find that collision-free motion sequence can always be generated when using a straight line as the intermediate configuration of transformation. The effectiveness of our method is demonstrated by both the simulation and the experimental tests taken on 3D printed chains. This work is supported by the National Science Foundation of China (61725204, 61432003, 61521002 and 61661130156) and the Royal Society-Newton Advanced Fellowship (NA150431). 2020-11-23T06:45:17Z 2020-11-23T06:45:17Z 2019 Journal Article Yu, M., Ye, Z., Liu, Y.-J., He, Y., & Wang, C. C. L. (2019). LineUp : computing chain-based physical transformation. ACM Transactions on Graphics, 38(1), 11-. doi:10.1145/3269979 0730-0301 https://hdl.handle.net/10356/144752 10.1145/3269979 1 38 en ACM Transactions on Graphics © 2019 Association for Computing Machinery. All rights reserved. |
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Engineering::Computer science and engineering Computing Methodologies Computing Graphics Yu, Minjing Ye, Zipeng Liu, Yong-Jin He, Ying Wang, Charlie Chang Ling LineUp : computing chain-based physical transformation |
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In this article, we introduce a novel method that can generate a sequence of physical transformations between 3D models with different shape and topology. Feasible transformations are realized on a chain structure with connected components that are 3D printed. Collision-free motions are computed to transform between different configurations of the 3D printed chain structure. To realize the transformation between different 3D models, we first voxelize these input models into a similar number of voxels. The challenging part of our approach is to generate a simple path—as a chain configuration to connect most voxels. A layer-based algorithm is developed with theoretical guarantee of the existence and the path length. We find that collision-free motion sequence can always be generated when using a straight line as the intermediate configuration of transformation. The effectiveness of our method is demonstrated by both the simulation and the experimental tests taken on 3D printed chains. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Yu, Minjing Ye, Zipeng Liu, Yong-Jin He, Ying Wang, Charlie Chang Ling |
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Article |
author |
Yu, Minjing Ye, Zipeng Liu, Yong-Jin He, Ying Wang, Charlie Chang Ling |
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Yu, Minjing |
title |
LineUp : computing chain-based physical transformation |
title_short |
LineUp : computing chain-based physical transformation |
title_full |
LineUp : computing chain-based physical transformation |
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LineUp : computing chain-based physical transformation |
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LineUp : computing chain-based physical transformation |
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lineup : computing chain-based physical transformation |
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2020 |
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https://hdl.handle.net/10356/144752 |
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1688665590813687808 |