Elastic parameter optimization has revealed its importance in 3D modeling, virtual reality, and additive manufacturing in recent years. Unfortunately, it is known to be computationally expensive, especially if there are many parameters and data samples. To address this challenge, we propose to introduce the inexactness into descent methods, by iteratively solving a forward simulation step and a parameter update step in an inexact manner. The development of such inexact descent methods is centered at two questions: 1) how accurate/inaccurate can the two steps be; and 2) what is the optimal way to implement an inexact descent method. The answers to these questions are in our convergence analysis, which proves the existence of relative error thresholds for the two inexact steps to ensure the convergence. This means we can simply solve each step by a fixed number of iterations, if the iterative solver is at least linearly convergent. While the use of the inexact idea speeds up many descent methods, we specifically favor a GPU-based one powered by state-of-the-art simulation techniques. Based on this method, we study a variety of implementation issues, including backtracking line search, initialization, regularization, and multiple data samples. We demonstrate the use of our inexact method in elasticity measurement and design applications. Our experiment shows the method is fast, reliable, memory-efficient, GPU-friendly, flexible with different elastic models, scalable to a large parameter space, and parallelizable for multiple data samples.
|Title of host publication||SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018|
|State||Published - Dec 4 2018|
|Event||SIGGRAPH Asia 2018 Technical Papers - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH Asia 2018 - Tokyo, Japan|
Duration: Dec 4 2018 → Dec 7 2018
|Name||SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018|
|Conference||SIGGRAPH Asia 2018 Technical Papers - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH Asia 2018|
|Period||12/4/18 → 12/7/18|
Bibliographical noteFunding Information:
This work was funded by NSF grant CHS-1524992 and NSFC grant 61332017. The authors would also like to thank Adobe and NVIDIA for additional equipment and funding supports.
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- Constrained optimization
- Inexact method
- Nonlinear elasticity
- Quasistatic simulation
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Human-Computer Interaction