Abstract. Point clouds are a digital representation of physical objects or buildings that exist in real world. There are many sources that a point cloud can come from such as a terrestrial laser scanner (TLS) or an unmanned aerial vehicle (UAV). This paper presents a simple method of integrating point clouds from two (2) data sources; TLS and UAV using simple alignment of rigid body transformation method known as Point Pair Picking (PPP). The point cloud data are the representation of details of a one-story building located in Johor Bahru, Malaysia. The process of aligning two (2) separate clouds into one (1) dataset requires initial processing such as noise removal before the alignment process was started. A laser (LAS) formatted data were formed so that it compatible with the PPP process. As the result, a high dense hybrid cloud-model was produced covering complete details of the building. This shows that integration of point clouds could improve 3D documentation assessment such as Building Information Modelling (BIM) by contributing richer semantic information.

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Geoinformation is a surveying and mapping field where topography and details on the ground are spatially mapped. The point cloud is one of the data types that is used for measurement and visualisation of Earth features mapping. Point cloud could come from a different source such as terrestrial laser scanned or photogrammetry. The concepts of terrestrial laser scanning and photogrammetry surveying are elaborated in this paper. This paper also presents the method used for point cloud registration; Iterative Closest Point (ICP) and Feature Extraction and Matching (FEM) and the accuracy of laser scanned, and photogrammetric point cloud based on the previous experiments. Experimental analysis conducted in the previous study shows an impressive result on laser scanned point cloud with very mínimum errors compared to photogrammetric point cloud.