Range image interpretation often suffers from contaminating noise and sparseness of the input data. Non-Gaussian errors occur if the physical conditions in the scene violate sensor restrictions. To deal with such drawbacks we present a new approach for range image preprocessing. To provide dense range information initial sparse data is augmented via appropriate interpolation. Furthermore, we propose a measure of plausibility which depends on the density of the initial data to judge the result of the interpolation.