ABC-GS：基于对齐的可控风格迁移用于3D高斯散射
ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting
AI总结：
本文提出ABC-GS框架，通过分割掩膜实现场景内容与风格特征的精准对齐，结合基于特征对齐的风格迁移损失函数，提升3D场景风格迁移的全局风格一致性与可控性。
摘要：
基于神经辐射场（NeRF）的3D场景风格化方法通过最近邻特征匹配（NNFM）损失优化取得良好效果，但NNFM损失未考虑全局风格信息，且NeRF的隐式表示限制了对生成场景的精细控制。本文引入ABC-GS，一种基于3D高斯散射的新框架，通过设计可控匹配阶段实现场景内容与风格特征的精确对齐，并提出基于特征对齐的风格迁移损失函数，确保风格迁移结果准确反映参考图像的全局风格。此外，通过深度损失和高斯正则化项保留场景的原始几何信息。大量实验表明，ABC-GS提供了风格迁移的可控性，并实现了更忠实于所选艺术参考图像全局风格的风格化结果。我们的主页可在https://vpx-ecnu.github.io/ABC-GS-website访问。
英文摘要：
3D scene stylization approaches based on Neural Radiance Fields (NeRF) achieve promising results by optimizing with Nearest Neighbor Feature Matching (NNFM) loss. However, NNFM loss does not consider global style information. In addition, the implicit representation of NeRF limits their fine-grained control over the resulting scenes. In this paper, we introduce ABC-GS, a novel framework based on 3D Gaussian Splatting to achieve high-quality 3D style transfer. To this end, a controllable matching stage is designed to achieve precise alignment between scene content and style features through segmentation masks. Moreover, a style transfer loss function based on feature alignment is proposed to ensure that the outcomes of style transfer accurately reflect the global style of the reference image. Furthermore, the original geometric information of the scene is preserved with the depth loss and Gaussian regularization terms. Extensive experiments show that our ABC-GS provides controllability of style transfer and achieves stylization results that are more faithfully aligned with the global style of the chosen artistic reference. Our homepage is available at https://vpx-ecnu.github.io/ABC-GS-website.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
