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We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient\u2010based optimizations. Our method requires no data pre\u2010processing, and minimal experimental set\u2010up, as we directly minimize the L2\u2010norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first marker\u2010free approach for calibrating a soft\u2010body simulator to match observed real\u2010world deformations. Our approach is inexpensive as it solely requires a consumer\u2010level LIDAR sensor compared to acquiring a professional marker\u2010based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi\u2010material scenarios of varying complexity. 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