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Learning Part Generation and Assembly for Sketching Man‐Made Objects
Computer Graphics Forum ( IF 2.5 ) Pub Date : 2020-12-16 , DOI: 10.1111/cgf.14184
Dong Du 1, 2, 3 , Heming Zhu 2, 3 , Yinyu Nie 2, 3, 4 , Xiaoguang Han 2, 3 , Shuguang Cui 2, 3 , Yizhou Yu 5 , Ligang Liu 1
Affiliation  

Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.

中文翻译:

学习零件生成和装配以绘制人造对象

在现有软件上对3D对象进行建模通常需要大量的交互,尤其是对于缺乏3D几何基本知识的用户。基于草图的建模是一种简化建模过程的解决方案,因此已经进行了数十年的研究。但是,对具有复杂结构的人造形状进行建模仍然具有挑战性。现有方法采用高级深度学习技术将整体草图映射到3D形状。它们仍然是处理复杂拓扑的瓶颈。在本文中,我们将sketch2shape的任务分解为零件生成模块和零件装配模块,其中利用深度学习方法来实现这两个模块。通过将焦点从整体形状更改为单个零件,可以简化形状生成器的学习过程并保证高质量的输出。借助学习到的自动化零件装配器,用户只需进行一点点手动调整即可获得所需的布局。大量的实验和用户研究证明了我们提出的系统的实用性。
更新日期:2021-02-24
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