With the rise of digital technology, the automated manufacturing capability for complex and fine models can be improved. For the 3D sculpture such as the process of fine design complex products, can be presented through the digital 3D model. In this paper, additional conditions are added as constraints on the basis of the original GAN generator, so that the generator and discriminator generate images or discriminate images according to the given conditions. At the same time, the diffusion model is utilized to remove the generated noise in the generated graphics. The 3D-GAN model is constructed by combining the body convolutional network with the generative adversarial network to generate 3D objects from the probability space. On this basis, the Transformer point cloud coding generation is introduced to refine the global features of the point cloud, and the Sigma model is used to reconstruct the 3D point cloud continuously from the sparse image of the sculpture points, so as to realize the reconstruction and optimization of the 3D modeling of the 3D sculpture point cloud. Using structural similarity and other indicators to evaluate the effect of 3D sculpture design, when is 0.5, the LPIPS value is the best, and the values are 0.1052, 0.1204, 0.1348, 0.1248 on four datasets, respectively, and comparing the error of the actual coordinate points and the coordinate points in the point cloud data, the error range is between pairs of 0.0001~0.0083, which indicates that sculpture point sparse image for 3D point cloud continuous reconstruction with better accuracy.
