SGSST：大规模高斯泼溅风格迁移
SGSST: Scaling Gaussian Splatting StyleTransfer
AI总结：
本文提出SGSST方法，通过基于全局神经统计量的多尺度损失SOS，首次在超高分辨率下对预训练3DGS场景实现高质量风格迁移，并在多项评估中表现优越。
摘要：
将风格迁移应用于完整的三维环境是一项具有挑战性的任务，自神经渲染出现以来已有许多进展。三维高斯泼溅（3DGS）最近在训练速度和重建质量方面进一步突破了许多神经渲染的局限。本文提出SGSST：大规模高斯泼溅风格迁移，这是一种基于优化的方法，用于将风格迁移应用于预训练的3DGS场景。我们证明了一种基于全局神经统计量的新型多尺度损失——我们将其命名为SOS（同时优化尺度）——能够将风格迁移应用于超高分辨率三维场景。SGSST不仅开创了如此高图像分辨率下的三维场景风格迁移，而且通过详尽的定性、定量和感知比较评估，其视觉质量也更为优越。
英文摘要：
Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms of training speed and reconstruction quality. This work introduces SGSST: Scaling Gaussian Splatting Style Transfer, an optimization-based method to apply style transfer to pretrained 3DGS scenes. We demonstrate that a new multiscale loss based on global neural statistics, that we name SOS for Simultaneously Optimized Scales, enables style transfer to ultra-high resolution 3D scenes. Not only SGSST pioneers 3D scene style transfer at such high image resolutions, it also produces superior visual quality as assessed by thorough qualitative, quantitative and perceptual comparisons.
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。
