Spatial data acquisition is rapidly developing, making point clouds available and easily accessible for many applications and to many end-users. Nowadays, point clouds are the main surveying product and have more added value than derived products as they keep details and they are not interpolated. Raw point clouds do not contain information that relates the points to the semantic meanings of the real word objects that are represented. Moreover, through different procedures (classification and segmentation techniques) important semantic information could be derived, creating an intelligent environment and structure, a Smart Point Cloud (SPC). In this research, a SPC framework will be created combining different techniques and methods in order to detect the pipes in an industrial environment. Close-range photogrammetry will be used to generate a point cloud, for which panoramic images are the main source data. The features and the attributes from both the data (panoramic images and point cloud) will be combined to get characteristics from both sources (2D & 3D) in order to select, analyze, manipulate and identify the pipes as one object.

This paper has its focus on the accessibility, interoperability and quality of different point cloud datasets. The main objective of the project is to research the process of harmonizing point cloud datasets of different origins and to incorporate the integrated datasets in a web viewer that allows for visualization and analysis. As a case study, the Three-Country Point (TCP) where Germany, Belgium, and the Netherlands share a border is evaluated.