3D imaging is a popular method for acquiring accurate models for a variety of applications. However, the size of the geometric features that can be modeled in this manner is dependant on the scanning system?s resolution. This paper presents a method that attempts to accurately reconstruct regions whose features are at or below the system?s scanning resolution, combining automatic region selection with a form of kriging. A curvature-based segmentation is followed by an automated geometry refinement procedure in which the model of spatial correlation between the irregularly sampled 3D data is automatically determined. Geometry refinement is done by a regularized kriging approach that is designed to preserve the sharp features typical to many 3D laser range applications. This method is validated on synthetic data, showing that the accuracy of our method is higher than that of its standard competitors. Then, the performance on real data is demonstrated through several examples.