AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting

3D Gaussian Splatting (3DGS) has emerged as a powerfulrepresentation for high-fidelity rendering. However, existing assets of-ten suffer from quality bottlenecks such as missing details and texturenoise. Prior attempts to enhance these assets via 2D image processingintroduce multi-view inconsistencies and high computational costs. Inthis paper, we propose a novel 3D-native refinement paradigm namedAnchorSplat. AnchorSplat is an end-to-end deep network operatingdirectly on 3D structures, avoiding the expensive optimization overheadof traditional 3D-2D-3D pipelines. Crucially, AnchorSplat is a strictlysource-free solution requiring no original multi-view images. Central tothe proposed method is the Point Anchor Mechanism, which enforcesgeometric consistency via local offset constraints, mitigating ill-posedmapping and gradient confounding. Furthermore, AnchorSplat replacesiterative densification with a single-pass multiplication mechanism. Tofacilitate research, we construct 3DGS-SR, the first large-scale bench-mark for this task. Experiments demonstrate state-of-the-art results onthe 3DGS-SR dataset, with throughput up to 105 times faster thanoptimization methods. Notably, AnchorSplat exhibits robust zero-shotgeneralization across diverse data distributions, including generativemodel outputs and real-world scans. The repository is available at: github