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.
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，自神经渲染兴起以来，这一领域取得了许多进展。近年来，三维高斯喷溅（3D Gaussian Splatting, 3DGS）在训练速度和重建质量方面进一步突破了神经渲染的许多限制。 本文提出了SGSST（Scaling Gaussian Splatting Style Transfer），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。我们设计了一种新的多尺度损失函数，基于全局神经统计信息，将其命名为SOS（Simultaneously Optimized Scales），使得风格迁移能够扩展到超高分辨率的三维场景。该方法不仅在高分辨率3D场景风格迁移上实现了突破，还在视觉质量方面表现卓越。 通过全面的定性、定量和感知比较，我们证明了SGSST在高分辨率三维场景风格迁移中表现出色，为实现更高质量和更逼真的3D环境风格化开辟了新方向。