3D-GIMP：当3D高斯图像修复与PatchMatch相遇
3D-GIMP: When 3D Gaussian Inpainting Meets PatchMatch
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中文总结 AI 辅助
针对3D场景编辑中迭代扩散模型的问题，提出3D-GIMP混合范式，通过在关键参考视图进行图像修复并利用3D感知PatchMatch算法传播纹理，优先保证重建一致性，在渲染速度和视图一致性上表现更优。
中文摘要 AI 辅助
近期3D场景编辑进展利用迭代扩散模型更新输入视图，但计算昂贵且难以生成清晰细节，还存在“幻觉漂移”导致多视图不一致。为此提出3D-GIMP，一种用于3D高斯点渲染中高保真对象移除的混合范式。在关键参考视图上进行单次生成式图像修复作为外观先验，通过3D感知PatchMatch算法经对应匹配在其余视图传播参考纹理，优先考虑重建一致性而非迭代生成，实验表明其在渲染速度和视图一致性上更优。
英文摘要
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.
发表机构
- University of Stuttgart（斯图加特大学）
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