3D Gaussian Splatting (3DGS) has recently transformed the fields of novel view synthesis and 3D reconstruction due to its ability to accurately model complex 3D scenes and its unprecedented rendering performance. However, a significant challenge persists: the absence of an efficient and photorealistic method for editing the appearance of the scene's content. In this paper we introduce VIRGi, a novel approach for rapidly editing the color of scenes modeled by 3DGS while preserving view-dependent effects such as specular highlights. Key to our method are a novel architecture that separates color into diffuse and view-dependent components, and a multi-view training strategy that integrates image patches from multiple viewpoints. Improving over the conventional single-view batch training, our 3DGS representation provides more accurate reconstruction and serves as a solid representation for the recoloring task. For 3DGS recoloring, we then introduce a rapid scheme requiring only one manually edited image of the scene from the end-user. By fine-tuning the weights of a single MLP, alongside a module for single-shot segmentation of the editable area, the color edits are seamlessly propagated to the entire scene in just two seconds, facilitating real-time interaction and providing control over the strength of the view-dependent effects. An exhaustive validation on diverse datasets demonstrates significant quantitative and qualitative advancements over competitors based on Neural Radiance Fields representations.

翻译：3D高斯泼溅（3DGS）因其能够精确建模复杂三维场景且具备前所未有的渲染性能，近期彻底改变了新视角合成与三维重建领域。然而，一个重大挑战依然存在：缺乏高效且逼真的方法来编辑场景内容的外观。本文提出VIRGi，一种能够快速编辑由3DGS建模场景色彩的新方法，同时保留镜面高光等视点依赖效果。我们方法的核心在于一种将色彩分解为漫反射与视点依赖分量的新型架构，以及整合多视角图像块的多视图训练策略。相较于传统的单视图批量训练，我们的3DGS表示提供了更精确的重建效果，并为重着色任务奠定了坚实的表示基础。针对3DGS重着色，我们进一步提出一种快速方案，仅需终端用户提供一张手动编辑的场景图像。通过微调单个MLP的权重，并配合可编辑区域的单次分割模块，色彩编辑能在两秒内无缝传播至整个场景，从而实现实时交互，并提供对视点依赖效果强度的控制。在多样化数据集上的全面验证表明，相较于基于神经辐射场表示的现有方法，本方法在定量与定性评估上均取得显著进步。