[Submitted on 1 Jul 2026]
Title:AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting
View a PDF of the paper titled AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting, by Dexu Zhu and 6 other authors
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Abstract:3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-fidelity rendering. However, existing assets often suffer from quality bottlenecks such as missing details and texture noise. Prior attempts to enhance these assets via 2D image processing introduce multi-view inconsistencies and high computational costs. In this paper, we propose a novel 3D-native refinement paradigm named AnchorSplat. AnchorSplat is an end-to-end deep network operating directly on 3D structures, avoiding the expensive optimization overhead of traditional 3D-2D-3D pipelines. Crucially, AnchorSplat is a strictly source-free solution requiring no original multi-view images. Central to the proposed method is the Point Anchor Mechanism, which enforces geometric consistency via local offset constraints, mitigating ill-posed mapping and gradient confounding. Furthermore, AnchorSplat replaces iterative densification with a single-pass multiplication mechanism. To facilitate research, we construct 3DGS-SR, the first large-scale benchmark for this task. Experiments demonstrate state-of-the-art results on the 3DGS-SR dataset, with throughput up to $10^5$ times faster than optimization methods. Notably, AnchorSplat exhibits robust zero-shot generalization across diverse data distributions, including generative model outputs and real-world scans.
Comments: Accepted by ECCV2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.01290 [cs.CV]
(or arXiv:2607.01290v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.01290
arXiv-issued DOI via DataCite
Submission history
From: Dexu Zhu [view email] [v1] Wed, 1 Jul 2026 12:14:29 UTC (4,128 KB)
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