The polygon model has a solid and smooth outer surface compared with a point cloud model, so it can be an effective solution for reconstructed image quality of the depth camera-based integral imaging system. Unlike the point cloud model, the polygon model consists of a many vertices and triangular mesh elements instead of points. First, each pixel of the depth information in Fig. 4(a) is matched up to a corresponding pixel in the color information, as shown in Fig. 4(b), where the dark circles represent the corresponded pixels (visible in both color and depth information), and the white circles represent the noncorresponded pixels (visible only in color information). Basically, if a conventional Delaunay triangulation algorithm has been applied, it would have generated the polygon model for the points with corresponding color information after the corresponded pixels are detected, as shown in Fig. 4(b).20 When the number of object points is given by , the entire process is performed in computational complexity. The Delaunay triangulation process requires a long processing time for a larger . So, in this paper, a simple triangulation method is proposed, and it is performed by arranging the vertices of the polygon model in grid form directly from the depth information. The triangulation results for depth information can be preserved as they are in color information. For example, from Figs. 4(a) and 4(b), when assuming that three neighboring points of depth information (, ), (, ), and (, ), which can be included in single triangle, are corresponded to (, ), (, ), and (, ) points of color information, respectively, the triangle (, ), (, ), and (, ) of depth data preserves the information of the triangle (, ), (, ), and (, ) in color information. The entire surface of the polygon model is generated using the neighboring vertical and/or horizontal vertices of the depth information, and it can save a great deal of processing time due to having a lower complexity than color information.