Correspondence-Free Material Reconstruction using Sparse Surface Constraints
Weiss S, Maier R, Cremers D, Westermann R, Thuerey N (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: IEEE Computer Society
Pages Range: 4685-4694
Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event location: Virtual, Online, USA
DOI: 10.1109/CVPR42600.2020.00474
Abstract
We present a method to infer physical material parameters, and even external boundaries, from the scanned motion of a homogeneous deformable object via the solution of an inverse problem. Parameters are estimated from real-world data sources such as sparse observations from a Kinect sensor without correspondences. We introduce a novel Lagrangian-Eulerian optimization formulation, including a cost function that penalizes differences to observations during an optimization run. This formulation matches correspondence-free, sparse observations from a single-view depth image with a finite element simulation of deformable bodies. In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme.
Involved external institutions
Germany (DE)How to cite
APA:
Weiss, S., Maier, R., Cremers, D., Westermann, R., & Thuerey, N. (2020). Correspondence-Free Material Reconstruction using Sparse Surface Constraints. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 4685-4694). Virtual, Online, USA: IEEE Computer Society.
MLA:
Weiss, Sebastian, et al. "Correspondence-Free Material Reconstruction using Sparse Surface Constraints." Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Virtual, Online, USA IEEE Computer Society, 2020. 4685-4694.
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