Existing Gaussian splatting methods struggle to achieve satisfactory novel view synthesis in driving scenes due to the lack of crafty design and geometric constraints of related elements. This paper introduces a novel method called Decoupled Hybrid Gaussian Splatting (DHGS), which aims at promoting the rendering quality of novel view synthesis for driving scenes. The novelty of this work lies in the decoupled and hybrid pixel-level blender for road and non-road layers, without conventional unified differentiable rendering logic for the entire scene, meanwhile maintaining consistent and continuous superimposition through the proposed depth-ordered rendering strategy. Beyond that, an implicit road representation comprised of 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 Waymo dataset prove that DHGS outperforms the state-of-the-art methods.
现有的高斯溅射方法在驾驶场景中难以实现令人满意的新视角合成，这是由于缺乏相关元素的巧妙设计和几何约束。本文提出了一种新颖的方法，称为解耦混合高斯溅射(Decoupled Hybrid Gaussian Splatting, DHGS)，旨在提高驾驶场景新视角合成的渲染质量。该工作的创新之处在于为道路和非道路层设计了解耦和混合的像素级混合器，而不是采用传统的整个场景统一可微渲染逻辑，同时通过提出的深度排序渲染策略保持一致和连续的叠加。 此外，我们训练了一个由符号距离场(Signed Distance Field, SDF)组成的隐式道路表示，用于监督具有微妙几何属性的道路表面。在辅助透明度损失和一致性损失的使用下，最终获得了具有难以察觉的边界和提高保真度的新颖图像。在 Waymo 数据集上进行的大量实验证明，DHGS 的性能优于现有最先进的方法。