SparseGS-W: Sparse-View Gaussian Splatting for Unconstrained Image Collections With Diffusion Priors

Synthesizing novel views from unconstrained image collections is an important but challenging task in computer vision. Existing methods, which optimize per-image appearance and transient occlusion through implicit neural networks from dense training views (approximately 1000 images), struggle to perform effectively with sparse inputs, resulting in noticeable artifacts. In this work, we introduce SparseGS-W, a novel framework designed to boost the reconstruction of unconstrained scenes and novel view synthesis using as few as five training images. Motivated by the observation that diffusion prior constrained by limited sparse inputs can remove artifacts through fast and efficient fine-tuning, we propose a plug-and-play Constrained Novel-View Enhancement module to iteratively enhance the quality of rendered novel views. We further present an Occlusion Handling scheme, which flexibly removes occlusions utilizing the inherent inpainting capability of constrained diffusion priors. Both components are capable of extracting appearance features from any user-provided reference image, enabling flexible modeling of illumination-consistent scenes. Extensive experiments demonstrate that SparseGS-W achieves superior performance not only in full-reference metrics, but also in commonly used non-reference metrics such as FID, ClipIQA and MUSIQ.