Differentiable Depth for Real2Sim Calibration of Soft Body Simulations |
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Authors: | K Arnavaz M Kragballe Nielsen P G Kry M Macklin K Erleben |
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Affiliation: | 1. University of Copenhagen, Copenhagen, Denmark;2. McGill University, Montreal, Canada;3. NVIDIA, Santa Clara, New Zealand |
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Abstract: | 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. |
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Keywords: | animation physically based animation methods and applications robotics rendering ray tracing |
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