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Smartsweep: context-aware modeling on a single image

Published:27 November 2017Publication History

ABSTRACT

Creating complementary objects for existing items is common in personal fabrication. However, most supportive systems focus more on 3D inputs [Weichel et al. 2014] and use less 2D information, even though images are easier to obtain than 3D objects in practice. Besides, even with a reference 3D model, it still requires tedious manual work to create a complementary object.

References

  1. Tao Chen, Zhe Zhu, Ariel Shamir, Shi-Min Hu, and Daniel Cohen-Or. 2013. 3-sweep: Extracting editable objects from a single photo. ACM Transactions on Graphics (TOG) 32, 6 (2013), 195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Manfred Lau, Greg Saul, Jun Mitani, and Takeo Igarashi. 2010. Modeling-in-context: user design of complementary objects with a single photo. In Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium. Eurographics Association, 17--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Tianjia Shao, Weiwei Xu, Kangkang Yin, Jingdong Wang, Kun Zhou, and Baining Guo. 2011. Discriminative Sketch-based 3D Model Retrieval via Robust Shape Matching. In Computer Graphics Forum, Vol. 30. Wiley Online Library.Google ScholarGoogle Scholar
  4. Christian Weichel, Manfred Lau, David Kim, Nicolas Villar, and Hans W Gellersen. 2014. MixFab: a mixed-reality environment for personal fabrication. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 3855--3864. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Smartsweep: context-aware modeling on a single image

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    • Published in

      cover image ACM Conferences
      SA '17: SIGGRAPH Asia 2017 Posters
      November 2017
      114 pages
      ISBN:9781450354059
      DOI:10.1145/3145690

      Copyright © 2017 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 November 2017

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      Overall Acceptance Rate178of869submissions,20%

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