Traditional visualization algorithms based on three-dimensional (3D) laser point cloud data consist of two steps: stripe point cloud data into different target objects and establish the 3D surface models of the target objects to realize visualization using interpolation point or surface fitting method. However, some disadvantages, such as low efficiency, loss of image details, exist in most of these algorithms. In order to cope with these problems, a 3D visualization algorithm based on space-slice is proposed in this paper, which includes two steps: data classification and image reconstruction. In the first step, edge detection method is used to check the parametric continuity and extract edges to classify data into different target regions preliminarily. In the second stage, the divided data is split further into space-slice according to coordinates. Based on space-slice of the point cloud data, one-dimensional interpolation methods is adopted to get the curves connected by each group of cloud point data smoother. In the end, these interpolation points obtained from each group are made by the use of getting the fitting surface. As expected, visual morphology of the objects is obtained. The simulation experiment results compared with real scenes show that the final visual images have explicit details and the overall visual result is natural.