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A Finite Difference Approximation of Second Order Regularization of Neural-SDFs
SessionGeometry
DescriptionWe propose a finite-difference curvature regularization for neural signed distance fields, achieving second-order accuracy by replacing Hessian terms with first-order information and matching state-of-the-art quality with reduced memory and runtime.