Abstract
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 this https URL.
Abstract (translated)
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）损失优化取得了令人鼓舞的结果。然而，NNFM损失没有考虑全局样式信息。此外，NeRF 的隐式表示限制了对结果场景的细粒度控制。在本文中，我们介绍了ABC-GS，这是一个基于3D高斯点绘的新框架，旨在实现高质量的3D风格转换。为此，设计了一个可控匹配阶段，通过分割掩码实现场景内容和样式特征之间的精确对齐。此外，提出了一种基于特征对齐的风格迁移损失函数，以确保风格迁移的结果准确反映参考图像的整体样式。另外，使用深度损失和高斯正则化项保留了原始场景的几何信息。广泛的实验表明，我们的ABC-GS提供了风格转换的可控性，并且实现了更加忠实于所选艺术参考的整体样式的风格化结果。更多关于ABC-GS的信息可以在[这里](https://example.com/)找到。
URL
https://arxiv.org/abs/2503.22218
