ReconFusion is a 3D reconstruction method that leverages diffusion priors to reconstruct real-world scenes from a limited number of photographs. It combines Neural Radiance Fields (NeRFs) with diffusion priors, enabling the synthesis of realistic geometry and textures at new camera poses beyond the input image set. This method is trained on diffusion priors with both limited-view and multi-view datasets, allowing it to synthesize realistic geometry and textures in unconstrained regions while preserving the appearance of the observed region. ReconFusion has been extensively evaluated on various real-world datasets, including forward and 360-degree scenes, demonstrating significant performance improvements.