Scientific data are often sampled at unstructured spatial locations because of physical constraints, yet most visualization software applies only to gridded or regular data. We discuss several effective techniques for representing scalar and vector-valued functions that interpolate to irregularly located data. Special attention is given to the situations in which the sampling domain is a 2D plane, a 3D volume, or a closed 3D surface. The interpolants can be evaluated on a fine regular grid and they can then be visualized with conventional techniques. Instead of giving a comprehensive survey of this subject, we concentrate on several methods that were developed in the last couple of years.