This pipeline utilizes the generative capabilities of 2D diffusion models and prompt self-repair to create panoramic images as the initial "flat" (2D) scene representation. Subsequently, it elevates this image to a 3D Gaussian function through carving techniques, enabling real-time exploration. To generate consistent 3D geometry, the pipeline constructs spatially consistent structure by transforming single-view depth into a globally optimized point cloud. This point cloud serves as the initial state for the 3D Gaussian function, aiding in addressing the occluding issues caused by single-view input. By applying semantic and geometric constraints to both the synthesized and input camera views, the pipeline guides the optimization of the Gaussian function to reconstruct unseen areas. Overall, this method provides a globally consistent 3D scene with a 360-degree view, enhancing the free experience offered by existing techniques.