StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors

3D Gaussian Splatting (3DGS) has achieved remarkable suc-cess in real-time novel view synthesis, yet it suffers from severe overfit-ting under sparse-view settings due to insufficient geometric constraints.While recent methods introduce monocular depth priors to mitigatethis, they inherently struggle with scale ambiguity and cross-view in-consistency, leading to defective geometry. In this paper, we proposeStereoGS, a novel sparse-view 3DGS framework that integrates stereopriors to establish reliable binocular consistency. Unlike scale-agnosticmonocular constraints, StereoGS introduces a Stereo Depth Regulariza-tion by constructing virtual stereo pairs during optimization and lever-aging a foundation stereo model to enforce absolute scale and binocular-consistent structures. To further suppress overfitting and eliminate re-dundant primitives, we design a Gradient-Aware Opacity Decay strat-egy that dynamically penalizes Gaussians based on their relative opacitygradient magnitudes. Combined with a Consistency-Aware Dense Ini-tialization using zero-shot multi-view depth estimation, StereoGS effec-tively anchors primitives to accurate scene surfaces. Extensive experi-ments on LLFF, DTU, Mip-NeRF360, and Blender datasets demonstratethat StereoGS achieves state-of-the-art performance in sparse-view set-tings without incurring any additional inference overhead. Project Page:https://stringerywh00.github.io/StereoGSprojectpage/