Generation of three dimensional photo-realistic models from Lidar and image data

Light detection and ranging (Lidar) instruments collect high density and accurate three dimensional (3D) point clouds of scanned surfaces of objects. 3D building modelling from terrestrial Lidar requires the raw point cloud data to be processed. Through processing, noise and outliers are eliminated from the point cloud, and a 3D photo-realistic model is generated using image data. This effectively reduces redundant data and enhances the visual representation. This paper deals with point cloud processing and proposes methods to automate several of the processing procedures. Specifically, we implemented automatic 3D point cloud registration, automatic target recognition used for geo-referencing, automatic plane detection algorithm used for surface modelling, and texture mapping. The proposed approach leads to the generation of accurately geo-referenced three dimensional (3D) photo-realistic models from point clouds and digital imagery.