DPR-Splat: Depth and Pose Refinement with Sparse-View 3D Gaussian Splatting for Novel View Synthesis

Abstract

Recent advances in 3D Gaussian Splatting have demonstrated impressive performance in novel view synthesis, particularly with dense image sets. However, its performance degrades significantly in sparse-view scenarios, primarily due to the challenge of obtaining accurate camera poses. Also, achieving scale-consistent and detailed depth maps is crucial, while existing depth estimation methods struggle to meet both requirements, further limiting view synthesis quality in sparse settings. To address these challenges, we propose DPR-Splat, an efficient neural reconstruction framework that builds 3D Gaussian models from sparse scenes. DPR-Splat refines the coarse outputs of MASt3R by leveraging dedicated pose and depth refinement modules, resulting in precise camera poses and depth maps. With the refined outputs, it progressively expands the 3D Gaussian set to construct an accurate scene model. Extensive experiments demonstrate that DPR-Splat enhances both novel view synthesis quality and pose estimation accuracy, and significantly accelerates training and rendering. Code and demonstration video are available at https://github.com/h0xg/DPR-Splat.