A 3D digital image model is proposed to represent the LIDAR data.The mathematical morphology is extended to 3D and then

dilation and erosion operators are re-defined.A method combining 3D mathematical morphology with clustering analysis is developed.Sequential dilation operations and clustering analysis are introduced into the 3D point cloud to achieve the pixel-level results of point cloud.The relationships between the two parameters and data property

resolution of point cloud and the minimum distance between objects

is discussed.Two case data are used to demonstrate the feasibility of the proposed method.The result for the first dataset is compared with those from the two other methods

Mean Shift algorithm and adaptive TIN filter method.The advantages and disadvantages are summarized using segmentation evaluation factors

segmentation accuracy

and computation efficiency.Meanwhile the stabilization of proposed method is also analyzed.