Differentiable Depth for Real2Sim Calibration of Soft Body Simulations
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In this work, we present a novel approach for calibrating material model parameters for soft body simulations using real data. We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient-based optimizations. Our method requires no data pre-processing, and minimal experimental set-up, as we directly minimize the L2-norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first marker-free approach for calibrating a soft-body simulator to match observed real-world deformations. Our approach is inexpensive as it solely requires a consumer-level LIDAR sensor compared to acquiring a professional marker-based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi-material scenarios of varying complexity. Finally, we show that our set-up can be extended to optimize for dynamic behaviour as well.
Originalsprog | Engelsk |
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Tidsskrift | Computer Graphics Forum |
Vol/bind | 42 |
Udgave nummer | 1 |
Sider (fra-til) | 277-289 |
Antal sider | 13 |
ISSN | 0167-7055 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This project has received funding from Independent Research Fund Denmark (DFF) under agreement No. 9131‐00085B.
Publisher Copyright:
© 2022 The Authors. Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.
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