A novel technique for LiDAR data segmentation and three-dimensional space projection

LiDAR (Light Detection And Sensing) data is a huge 3-dimensional data which is difficult to explore information from the data. It is necessary to segment this LiDAR data. In this paper, segmentation is done using quad-tree structure. In each segment, a point cloud is obtained. Subsequently we create a Triangulated Irregular Network (TIN) for each point cloud. Later TIN is merged to obtain structural lines for a given LiDAR data. The initial result helps us understand and reveal relevant information about the objects/shapes from the raw benchmark LiDAR dataset, i.e., ALS data of Toronto.