In recent years, there has been increasing interest in applying stylization on 3D scenes from a reference style image, in particular onto neural radiance fields (NeRF). While performing stylization directly on NeRF guarantees appearance consistency over arbitrary novel views, it is a challenging problem to guide the transfer of patterns from the style image onto different parts of the NeRF scene. In this work, we propose a stylization framework for NeRF based on local style transfer. In particular, we use a hash-grid encoding to learn the embedding of the appearance and geometry components, and show that the mapping defined by the hash table allows us to control the stylization to a certain extent. Stylization is then achieved by optimizing the appearance branch while keeping the geometry branch fixed. To support local style transfer, we propose a new loss function that utilizes a segmentation network and bipartite matching to establish region correspondences between the style image and the content images obtained from volume rendering. Our experiments show that our method yields plausible stylization results with novel view synthesis while having flexible controllability via manipulating and customizing the region correspondences.

我们提出了一种基于局部风格转移的NeRF风格化框架，利用哈希网格编码来学习外观和几何组件的嵌入，并通过优化外观分支并保持几何分支固定来实现风格化，使用分割网络和二分图匹配来建立样式图像和内容图像之间的区域对应关系，实验证明我们的方法在新视角合成方面得到了合理的风格化结果，并通过操纵和自定义区域对应关系具有灵活的可控性。

本地样式化的神经光辐射场