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Résumé : Single-view scene reconstruction presents significant challenges due to the limited viewpoint information. Panorama images, as a form of image representation with a 360$^\circ$ field of view (FoV), offer a richer source of information for scene reconstruction. Therefore, more and more work utilizes a single panorama image for scene reconstruction. However, existing methods for scene reconstruction from a single panorama image often fail to adequately address the unique characteristics of panorama images and overlook the distinctions between panorama and limited FoV images.In this paper, we propose a novel method for inpainting and reconstructing scenes from a single panorama image. By capitalizing on the distinctive projection characteristics of panorama images and the irregularity of inpainting regions, we develop a panorama inpainting algorithm that incorporates a specialized inpainting strategy and fine-tunes a diffusion model to enhance the quality of inpainting for a single image. Additionally, we exploit the consistency of existing geometry to establish a multi-view consistent denoising process, ensuring coherence among images captured from different viewpoints. Through our two-stage inpainting strategy, we successfully transform the scene geometry derived from a single panorama image into a 3D Gaussian Splatting representation. Comprehensive qualitative and quantitative evaluations demonstrate the superiority of our proposed method compared to state-of-the-art single-view scene reconstruction methods.