Feed-forward 3D Gaussian Splatting methods have achieved impressive reconstruction quality for autonomous driving scenes, yet they entangle scene geometry with transient appearance properties such as lighting, weather, and time of day. This coupling prevents relighting, appearance transfer, and consistent rendering across multi-traversal data captured under varying environmental conditions. We present SpectralSplat, a method that disentangles appearance from geometry within a feed-forward Gaussian Splatting framework. Our key insight is to factor color prediction into an appearance-agnostic base stream and and appearance-conditioned adapted stream, both produced by a shared MLP conditioned on a global appearance embedding derived from DINOv2 features. To enforce disentanglement, we train with paired observations generated by a hybrid relighting pipeline that combines physics-based intrinsic decomposition with diffusion based generative refinement, and supervise with complementary consistency, reconstruction, cross-appearance, and base color losses. We further introduce an appearance-adaptable temporal history that stores appearance-agnostic features, enabling accumulated Gaussians to be re-rendered under arbitrary target appearances. Experiments demonstrate that SpectralSplat preserves the reconstruction quality of the underlying backbone while enabling controllable appearance transfer and temporally consistent relighting across driving sequences.

翻译：前馈式三维高斯泼溅方法在自动驾驶场景的重建质量上已取得显著成效，但其将场景几何与瞬态外观属性（如光照、天气、时段）纠缠在一起。这种耦合阻碍了重光照、外观迁移以及多遍历数据（在不同环境条件下采集）的连贯渲染。我们提出SpectralSplat——一种在前馈高斯泼溅框架内实现外观与几何解耦的方法。核心见解在于将颜色预测分解为与外观无关的基础流和受外观条件约束的适配流，两者均由共享的多层感知机（MLP）生成，该MLP以源自DINOv2特征的全局外观嵌入为条件。为强制实现解耦，我们采用混合重光照流水线生成的配对观测数据进行训练，该流水线结合了基于物理的固有分解与基于扩散的生成式精炼，并通过互补一致性损失、重建损失、跨外观损失及基础颜色损失进行监督。进一步引入外观自适应时序历史机制，存储与外观无关的特征，使累积的高斯体能够在任意目标外观下重渲染。实验表明，SpectralSplat在保持骨干网络重建质量的同时，实现了受控外观迁移及跨驾驶序列的时间一致性重光照。