Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

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.