让3D高斯点云同时迁移风格与几何结构，效果更真实稳定。
Geometry-Aware Style Transfer in 3D Gaussian Splatting
- 分步优化颜色与几何参数，避免互相干扰。
- 新方法在定量指标上显著优于现有3DGS风格化方案。
- 适合需要精准几何保持的3D内容生成场景。
本文提出一种新型几何感知的3D高斯点云风格迁移框架，可同步迁移外观属性与几何结构。不同于以往仅关注色彩风格且忽略结构适配的方法，本方法通过解耦优化策略，交替更新颜色与几何参数，有效缓解两者间的干扰，实现稳定一致的全局几何变换。该策略由提出的几何感知对比特征匹配（GCFM）支持，GCFM将RGB、深度和边缘信息融合至对比目标中，并在两个优化阶段均使用，以高效地将风格图像的结构特征传递给高斯原型。大量实验证明，该方法在定性保真度与定量指标上均表现优异，显著超越现有基于3DGS的风格化方法。代码已开源：https://github.com/oweixx/gast。
原文摘要 · Abstract (English)
In this paper, we present a novel geometry-aware style transfer framework for 3D Gaussian splatting (3DGS) that simultaneously transfers appearance attributes and geometric structures. Unlike prior works that primarily focus on color-based stylization and often overlook structural adaptation, our method explicitly incorporates geometry adaptation through a decoupled optimization scheme that alternately updates color and geometry parameters. This strategy alleviates potential interference between color and geometry updates, leading to stable and consistent scene-level geometry transformation. The decoupled optimization is enabled by the proposed geometry-aware contrastive feature matching (GCFM). GCFM integrates RGB, depth, and edge cues into a contrastive objective and is employed in both optimization phases to effectively transfer structural characteristics from style images to Gaussian primitives. Extensive experiments show that our approach achieves superior performance in both qualitative fidelity and quantitative metrics, significantly outperforming existing 3DGS-based stylization methods. Our code is available at \href{https://github.com/oweixx/gast}{https://github.com/oweixx/gast}.
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