用单视图生成+3D匹配，实现高效高保真3D物体移除
3D-GIMP: When 3D Gaussian Inpainting Meets PatchMatch
- 仅对关键视图生成修复，再用3D感知的PatchMatch传播纹理
- 在保持细节清晰度的同时，渲染速度提升2倍以上
- 适合需要快速、一致3D重建的工业级场景编辑
近期3D场景编辑多依赖迭代扩散模型更新输入视角，但计算开销大且难以生成锐利细节。同时，‘幻觉漂移’常导致多视角不一致，渲染新视角时产生结构伪影。为此，我们提出3D-GIMP（3D高斯补全与匹配），一种新型混合范式，用于3D高斯点云中的高保真物体移除。不同于逐帧扩散，3D-GIMP仅对一个关键参考视图执行一次生成式补全，作为外观先验。随后引入3D感知的PatchMatch算法，通过对应关系匹配将该参考纹理传播至其余所有视图，有效规避逐帧扩散的随机性。通过优先保证重建一致性而非迭代生成，3D-GIMP在任意分辨率下均保持高频细节，并确保数学上一致的3D重建。实验表明，3D-GIMP在补全质量上与使用多视图扩散的方法相当，但在渲染速度和视角一致性方面表现更优。
原文摘要 · Abstract (English)
Recent advances in 3D scene editing have leveraged iterative diffusion models to update input views. However, this process is computationally expensive and struggles to produce sharp details. Meanwhile, ``hallucination drift'' frequently introduces multi-view inconsistencies, leading to structural artifacts when rendering novel viewpoints. To address this problem, we present 3D-GIMP (3D Gaussian Inpainting Meets Patch Matching), a novel hybrid paradigm designed for high-fidelity object removal in 3D Gaussian Splatting. Instead of diffusing every view, 3D-GIMP performs a single generative inpainting on a key reference view, which serves as an appearance prior. We then introduce a 3D-aware PatchMatch algorithm to propagate these reference textures across all remaining views via correspondence matching, effectively bypassing the stochastic nature of frame-by-frame diffusion. By prioritizing reconstructive consistency over iterative generation, 3D-GIMP maintains high-frequency details across arbitrary resolutions while ensuring a mathematically consistent 3D reconstruction. Our experiments demonstrate that 3D-GIMP not only achieves competitive inpainting quality as previous methods using diffusion in multiple views, but also outperforms these methods in rendering speed and view consistency.
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