Boundary-Aware Multidomain Subspace Deformation

By Y. Yang, Weiwei Xu, X. Guo, Kun Zhou, Baining Guo. 2013

57 citations recorded by the source corpus.

In this paper, we propose a novel framework for multidomain subspace deformation using node-wise corotational elasticity. With the proper construction of subspaces based on the knowledge of the boundary deformation, we can use the Lagrange multiplier technique to impose coupling constraints at the boundary without overconstraining. In our deformation algorithm, the number of constraint equations to couple two neighboring domains is not related to the number of the nodes on the boundary but is the same as the number of the selected boundary deformation modes. The crack artifact is not present in our simulation result, and the domain decomposition with loops can be easily handled. Experimental results show that the single-core implementation of our algorithm can achieve real-time performance in simulating deformable objects with around quarter million tetrahedral elements.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Xiaowu Chen
2. Kan Guo
3. Dongqing Zou
4. Panpan Shui
5. Fenggen Yu
6. Y. Gan
7. Charles Ruizhongtai Qi
8. Leonidas J. Guibas

In Computer Graphics and Computer-Aided Design

1. Xiaowu Chen
2. Kan Guo
3. Dongqing Zou
4. Charles Ruizhongtai Qi
5. Leonidas J. Guibas
6. Hao Su

Citations in the corpus

• 3D shape segmentation via shape fully convolutional networks (2017, background): “quisite to explore the inherent characteristics of the shape. The results obtained from shape segmentation can be applied to various fields of computer graphics, such as shape editing [1], deformation [2], and modelling [3]. Shape segmentation, therefore, has become a research hotspot, yet diculties ”
• 3D Mesh Labeling via Deep Convolutional Neural Networks (2015, methodology): “…that each triangle on the mesh belongs to This step, denoted as mesh labeling, aims to infer the inherent characteristics of the mesh and can be applied to various applications such as mesh editing [Yu et al. 2004; Chen et al. 2015a],modelling [Chenetal.2013,2015b]anddeformation[Yang et al. 2013 .”