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We propose a method of computing surface curvature properties from the signed distance field (SDF) samples in the 3D space. The SDF representation contains information of the surface normal at the closest point on the surface from the sampling point. The variance of these information from different sampling points within the neighborhood reflects the curvature information. Because this sampling is done in the 3D space, we do not directly referees to the parametric surface coordinates or polygon structures. The computation is stable because it requires only linear algebraic operations. It is possible to extract multiple scale curvatures by changing sampling interval. The proposed method was applied on real data, and result of multiscale curvature extraction is presented.