Make-It-3D converts a single input image into a complete 3D representation by leveraging prior knowledge from a 2D diffusion model. The method operates in two stages: first, it optimizes a neural radiance field using constraints from the reference image at the frontal view and diffusion prior at novel viewpoints; second, it converts the coarse model into textured point clouds and enhances realism using the diffusion prior. The approach enables creation of 3D geometry with hallucinated unseen textures.