S2D-Splat: Static-First Gaussian Reconstruction of Dynamic Driving Scenes with Layer-Wise Feature Guidance

Moving objects introduce cross-frame inconsistencies that complicate camera estimation and static geometry recovery in driving scenes. Recent feed-forward Gaussian methods explicitly separate static and dynamic primitives, but often estimate camera parameters and geometry from shared features that jointly encode both components, leaving static reconstruction susceptible to dynamic interference. To address this challenge, we propose S2D-Splat, a feed-forward framework for static-first Gaussian reconstruction with layer-wise feature guidance. Our framework separates static and dynamic features before camera and geometry estimation, enabling the static branch to recover camera parameters and static 3D Gaussians with reduced dynamic interference. Intermediate static features are cached at each layer and reused as fixed conditioning at corresponding layers of the dynamic branch. This asymmetric information flow preserves the established static representations while providing stable geometric context for reconstructing dynamic 3D Gaussians. An instance-level motion decoder further queries the fused static and dynamic representations to estimate object trajectories, supporting motion-aligned Gaussian fusion and temporal interpolation. Experiments on Waymo and zero-shot evaluations on nuScenes and the MM-AU accident dataset demonstrate that S2D-Splat outperforms existing approaches on most evaluated metrics and generalizes to highly dynamic, safety-critical driving scenarios.