DHGS: 面向驾驶场景的解耦混合高斯溅射
DHGS: Decoupled Hybrid Gaussian Splatting for Driving Scene
AI总结：
针对现有高斯溅射方法在驾驶场景新视角合成效果不佳的问题，提出DHGS解耦混合高斯溅射方法，通过分层混合渲染、SDF隐式道路表示及辅助损失提升渲染质量，在Waymo数据集上性能超越现有最优方法。
摘要：
现有的高斯溅射方法在驾驶场景的新视角合成中往往难以取得令人满意的效果，主要原因在于缺乏针对场景内相关元素的精巧设计与几何约束。本文提出了一种名为解耦混合高斯溅射（Decoupled Hybrid Gaussian Splatting, DHGS）的新型神经渲染方法，旨在提升静态驾驶场景新视角合成的渲染质量。本研究的创新点在于针对道路层与非道路层采用了解耦混合的像素级混合器，而非传统的针对整个场景的统一可微渲染逻辑；同时通过提出的深度有序混合渲染策略，保证了叠加过程中的一致性与连续性。此外，本文还训练了由有符号距离函数（Signed Distance Function, SDF）构成的隐式道路表示，用以监督具有精细几何属性的路面。结合辅助透射率损失与一致性损失的使用，最终获得了边界难以察觉、保真度更高的新视角图像。在Waymo数据集上开展的大量实验证明，DHGS的性能优于现有最优方法。提供更多视频佐证的项目页面为：https://ironbrotherstyle.github.io/dhgs_web。
英文摘要：
Existing Gaussian splatting methods often fall short in achieving satisfactory novel view synthesis in driving scenes, primarily due to the absence of crafty designs 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. Still, consistency and continuity in superimposition are preserved through the proposed depth-ordered hybrid rendering strategy. Additionally, an implicit road representation comprised of a Signed Distance Function (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
