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The Cramer-Rao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The Cramer-Rao error bound has been applied to scalar- and vector-valued estimators and recently to parametric shape estimators. However, nonparametric, low-level surface representations are an important tool in 3D reconstruction, and are particularly useful for representing complex scenes with arbitrary shapes and topologies. We present a generalization of the Cramer-Rao error bound to nonparametric shape estimators. Specifically, we derive the error bound for the full 3D reconstruction of scenes from multiple range images.