Point clouds are becoming one of the most common ways to represent geographical data. The scale of acquisition of point clouds is growing steadily. However, point clouds are often very large in storage size and require computationally intensive operations. The integration of point clouds nowadays still face a lot of challenges. This project focuses on one of these challenges; integrating point clouds of different scales and granularity. Solving this challenge enables appealing visualisation, usability for low and high computation powers and geometrical consistency for analysis. The following question is researched: 'To what extent can a vario-scale approach improve integration of point clouds with varying point densities?'. A data model is created that uses importance as an additional dimension. This dimension contains an importance value which is calculated using two methods. Firstly random assignment of values to the points and secondly exact computed values. To compute this value the smallest distances to its nearest neighbour is assigned as importance value. A web application shows the results. Both random and exact methods show an exponential decay in distribution of the importance value. Though the random methods run much faster, the exact methods preserve much more edges and other details.