EviSR-GS：Evidence-Guided Supervision and Refinement for Sparse-View Gaussian Splatting

Sparse-view Gaussian splatting faces spatially uneven constraints from limited observations. Stronger pixelwise fitting can reinforce ambiguous reconstructions, while regions that could benefit from additional capacity may lack reliable cues for Gaussian placement. We propose EviSR-GS, an evidence-guided framework that combines spatially adaptive supervision with provisional Gaussian refinement, separating capacity allocation from subsequent activity assessment. For supervision, local depth residuals and image–depth gradient mismatch modulate the relative weights of pixelwise RGB reconstruction and patch-level perceptual losses. For refinement, low rendered coverage or disagreement with a fixed depth prior identifies candidate regions. Cross-view depth statistics and current coverage prioritize depth-based Gaussian proposals under a per-event insertion budget. Newly inserted Gaussians are jointly optimized with the existing representation and protected from ordinary pruning during a maturation period. Afterwards, additions with both low opacity and low accumulated rendering visibility undergo soft opacity attenuation. Experiments on LLFF, DTU, and Mip-NeRF 360 demonstrate competitive novel-view synthesis performance. On three-view LLFF, the full framework improves PSNR by 0.69 dB and reduces LPIPS by 0.021 relative to a baseline with both branches disabled. Ablations further support the contributions of both branches and the benefit of spatially varying loss weights over fixed scene-level weights. Code will be made publicly available.