Flash-Refine: Frustum-Guided Local Incremental Learning for Efficient 3D Gaussian Splatting Completion

While 3D Gaussian Splatting (3DGS) has enabled real-time high-fidelity novel view synthesis, maintaining already-deployed static 3DGS assets remains difficult. When a local region is missing or blurred, retraining the whole scene from scratch is costly, while directly finetuning the asset on newly captured repair images causes severe degradation of unobserved views, and geometric stitching introduces boundary and depth conflicts. We propose Flash-Refine, a practical in-situ repair pipeline for static 3DGS assets that operates directly on a native PLY model without access to historical training images. Given newly registered repair views, Flash-Refine uses Point-Wise Consensus Masking and an Adaptive Depth-Bounded Prior to identify a bounded active region, applies Gradient Locking to frozen background Gaussians, propagates the mask through native densification, and protects frozen Gaussians from close-up pruning. Under this constrained active set, gradient-gated densification naturally allocates new parameters to the defective region, enabling rapid local completion from peripheral seeds. Extensive experiments on Deep Blending and Mip-NeRF 360 show that Flash-Refine achieves the best Dropped and Kept PSNR among practical repair methods across four scenes, while requiring only 2–4 minutes on an RTX 4090. We further analyze the method’s scope and limitations: it assumes static scenes and preregistered repair views, and although frozen background parameters remain unchanged, newly densified active Gaussians may still induce scene-dependent visibility interactions in unobserved views.