LiDAR technologies are used to measure point cloud data of the earth's surface. The usage of LiDAR allows for the fast collections of massive data sets. The AHN2 point cloud data set, part of Rijkswaterstaats initiative to map the surface of the Netherlands, contains 639 478 217 460 points. For efficient visualization in web viewers, these massive point clouds are stored in an octree data structure. Visualization through this method has the downside of discretely visualizing the point cloud. These discrete artefacts are referred to as density jumps, and are visible where there is a boundary between blocks retrieved from the octree. These blocks contain different densities because they are retrieved from different levels of the octree. This thesis proposes a continuous visualization method for massive point cloud data sets that aims to eliminate these density jumps. While the continuous visualization of vector data sets has been extensively researched, this is a novel field of research for point cloud data sets. This thesis explores the feasibility of a vario-scale visualization method, and aims to implement it in an existing web viewer architecture. Due to the massive nature of the AHN2 data set, cloud computing and distributed computing techniques are used to imrove the workflow. The presented methodology removes the \textit{density jumps} by determining an upper density bound for the point cloud density relative to the camera position. Circle packing theory is used to reinforce the upper bound continuously, thus removing artefacts created by discrete density jumps. A proof-of-concept for this theory is implemented in an existing point cloud web viewer architecture.

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.