Correspondence-Free Material Reconstruction using Sparse Surface Constraints

Sebastian Weiss, Robert Maier, Daniel Cremers, Rudiger Westermann, Nils Thuerey; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 4686-4695

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.

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[bibtex]
@InProceedings{Weiss_2020_CVPR,
author = {Weiss, Sebastian and Maier, Robert and Cremers, Daniel and Westermann, Rudiger and Thuerey, Nils},
title = {Correspondence-Free Material Reconstruction using Sparse Surface Constraints},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}