ReInGS: Re-Initializing 3D Gaussians against Sparsity Discrepancy in Few-Shot Novel View Synthesis

Recent advancements in 3D Gaussian Splatting (3DGS) haveachieved significant progress in novel view synthesis (NVS), offering com-putationally efficient and photorealistic rendering, even in the few-shotsetting where input training views are sparse. However, several repre-sentative 3DGS-based methods for few-shot NVS face a critical issue ofsparsity discrepancy, i.e., Gaussian initialization leverages dense fusedstereo points from extended views, creating an inconsistency with sparsetraining views. To resolve this issue, we propose ReInGS, a novel methodfeaturing a two-stage coarse-to-fine 3D Gaussian re-initialization pro-cess designed explicitly for few-shot NVS. In the coarse-level initializa-tion stage, an expansion-based hybrid point sampling (EHPS) strategyis deployed to generate dense points and effectively capture the scene’sglobal geometric structure. In the fine-level re-initialization stage, a view-dominant details-aware point sampling (VDPS) strategy, which com-prises two sequential sub-strategies on intra-view point sampling andcross-view point sampling, is adopted to reconstruct the scene’s localgeometric details with high fidelity. Comprehensive experiments on twobenchmarking datasets demonstrate that ReInGS achieves state-of-the-art performance across 2-view, 3-view, and 4-view settings, especiallydelivering PSNR gains of up to 2.92 dB, 1.92 dB, and 1.38 dB, respec-tively, while synthesizing high-quality novel views without data leakagefrom extended views.