风格化 Gaussian Splatting
Gaussian Splatting in Style
AI总结：
本文提出基于 3D Gaussian splatting、多分辨率哈希网格和风格条件小型 MLP 的实时 3D 场景风格化架构，可跨风格泛化并在真实室内外数据上达到 SOTA。
摘要：
3D 场景风格化将神经风格迁移的工作扩展到了 3D。该问题中的一个关键挑战是在多个视图之间保持风格化外观的一致性。以往绝大多数工作通过为每张风格化图像和一组多视图图像训练一个 3D 模型来实现这一点。相比之下，我们提出了一种在风格图像集合上训练的新颖架构，在测试时可实时生成高质量的风格化新视图。我们为模型选择的底层 3D 场景表示是 3D Gaussian splatting。我们获取 3D 高斯，并使用多分辨率哈希网格和一个小型 MLP 对其进行处理，以获得风格化视图。该 MLP 以不同的风格代码为条件，从而在测试时泛化到不同风格。3D 高斯的显式特性赋予我们相对于基于 NeRF 的方法的内在优势，包括几何一致性以及快速训练和渲染机制。这使我们的方法可用于多种实际用例，例如增强现实或虚拟现实。我们证明，在各种室内和室外真实世界数据上，本方法以更优的视觉质量实现了最先进性能。
英文摘要：
3D scene stylization extends the work of neural style transfer to 3D. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across multiple views. A vast majority of the previous works achieve this by training a 3D model for every stylized image and a set of multi-view images. In contrast, we propose a novel architecture trained on a collection of style images that, at test time, produces real time high-quality stylized novel views. We choose the underlying 3D scene representation for our model as 3D Gaussian splatting. We take the 3D Gaussians and process them using a multi-resolution hash grid and a tiny MLP to obtain stylized views. The MLP is conditioned on different style codes for generalization to different styles during test time. The explicit nature of 3D Gaussians gives us inherent advantages over NeRF-based methods, including geometric consistency and a fast training and rendering regime. This enables our method to be useful for various practical use cases, such as augmented or virtual reality. We demonstrate that our method achieves state-of-the-art performance with superior visual quality on various indoor and outdoor real-world data.
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。
