  Existing Gaussian splatting methods often fall short in achieving
satisfactory novel view synthesis in driving scenes, primarily due to the
absence of crafty design and geometric constraints for the involved elements.
This paper introduces a novel neural rendering method termed Decoupled Hybrid
Gaussian Splatting (DHGS), targeting at promoting the rendering quality of
novel view synthesis for static driving scenes. The novelty of this work lies
in the decoupled and hybrid pixel-level blender for road and non-road layers,
without the conventional unified differentiable rendering logic for the entire
scene, while still maintaining consistent and continuous superimposition
through the proposed depth-ordered hybrid rendering strategy. Additionally, an
implicit road representation comprised of a Signed Distance Field (SDF) is
trained to supervise the road surface with subtle geometric attributes.
Accompanied by the use of auxiliary transmittance loss and consistency loss,
novel images with imperceptible boundary and elevated fidelity are ultimately
obtained. Substantial experiments on the Waymo dataset prove that DHGS
outperforms the state-of-the-art methods. The project page where more video
evidences are given is: https://ironbrotherstyle.github.io/dhgs_web.
