Model Structure from Laser Scanner Point Clouds

The essential target of this research work is generating a three-dimensional (3D) design of cloud point laser scanner structure that performs computer vision system (CVS) processes intentionally fully capabilities of invest to estimate model structure resulting from laser scanner point clouds and recognizing main areas for further studies and development. This study is significant processes were displayed, begins from collecting the clouds of laser point in static mode, and then analyzing the existing data, an iterative closest point algorithm (ICP) used in point cloud registration and matching, noise reduction, synthesis scheme in laser scanner point clouds uses a random sample consensus algorithm (RANSAC), data generating 3D laser scanner, importing a structure model from laser scanner point clouds and fit in using CVS programs. The Poisson surface algorithm is utilized to pick up point clouds and mesh surfacing. The results displaced some of the structural troubles such as deformation and cracks over two places of the wall. The results of the proposed algorithm matched the original point clouds very well. This differential proved that the algorithm advanced in this research work is efficient and more workable for visualization, monitoring, and future data processing.