Point cloud data are important sources for 3D geo-information. The point cloud data sets are growing in popularity and in size. Modern Big Data acquisition and processing technologies, such as laser scanning from airborne, mobile, or static platforms, dense image matching from photos, multi-beam echo-sounding, or from autotracking seismic data, have the potential to generate point clouds with millions or billions (or even trillions) of 3D points (with in many cases one or more attributes attached). This is especially true with the available and expected repeated scans of same area (the temporal dimension). These point clouds are too massive to be handled efficiently by common geo-ICT infrastructures. At the database level, initial implementations are available in both commercial and open source database products, illustrating the user need for point cloud support; e.g. Oracle spatial’s SDO_PC data type and PostgreSQL/PostGIS PCPATCH data type. This new data type should be available in addition to the existing vector and raster data types. Further, a new and specific web-services protocol for point cloud data is investigated, supporting progressive transfer based on multi-resolution. The eScience project investigates solutions in order to better exploit the rich potential of point cloud data. The project partners are: Rijkswaterstaat (RWS), Fugro, Oracle, Netherlands eScience Centre and TU Delft. An inventory of the user requirements has been made using structured interviews with users from different background: government, industry and academia. Based on these requirements a benchmark has been developed to compare various point cloud data management solutions w.r.t. functionality and performance. The main test data set is the second national height map of the Netherlands, AHN2, with 6 to 10 samples for every square meter of the country, resulting in more than 600 billion points with 3 cm accuracy.