将3D高斯点云分解为静态背景与可操作动态物体，实现自动驾驶闭环测试的高保真实时仿真。
DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing
- 用对象中心表示法分离静态背景与可操控动态物体，提升场景交互性。
- 实现实时交通渲染、轨迹严格对齐和光照无缝融合，保持视觉一致性。
- 适合需要高保真闭环仿真的端到端自动驾驶算法评测，尤其关注动态场景建模。
端到端（E2E）自动驾驶算法需要在高视觉保真度、强交互性和实时性能的仿真环境中进行严格闭环验证。现有方法从游戏引擎到静态神经渲染，均在这些需求间存在权衡，难以应对E2E测试所需的动态场景构建。为此，我们提出一种面向大规模E2E评估的解耦3D高斯点云（3DGS）框架。通过对象中心的规范表示，将场景从根本上分解为高保真静态背景与可操控动态代理。为解决由此产生的表示冲突，引入三个针对性模块：(1) 基于感知剪枝与向量量化的内容压缩，实现交通实时渲染；(2) 利用语义拓扑的地图引导几何配准，严格对齐轨迹；(3) 基于代理的再照明，实现环境光照的无缝迁移。大量实验表明，DecoupleGS实现了保真度与效率的平衡，提升了度量与光度一致性，提供了一个实用的闭环传感器仿真平台，适用于E2E自动驾驶评估。
原文摘要 · Abstract (English)
End-to-end (E2E) autonomous driving algorithms require rigorous closed-loop validation in simulation environments offering high visual fidelity, strong interactivity, and real-time performance. Existing approaches, from game engines to static neural rendering, inherently trade off these requirements and struggle with the dynamic scene composition essential for E2E testing. To bridge this gap, we propose a novel decoupled 3D Gaussian Splatting (3DGS) framework tailored for large-scale E2E evaluation. We fundamentally decompose scenes into a high-fidelity static background and manipulable dynamic agents using an object-centric canonical representation. To resolve resulting representational conflicts, we introduce three targeted modules: (1) asset compression via perceptual pruning and vector quantization for real-time traffic rendering; (2) map-guided geometric registration leveraging semantic topology to strictly align trajectories; and (3) proxy-based relighting transferring ambient illumination for seamless photometric integration. Extensive experiments demonstrate that DecoupleGS achieves a balanced fidelity-efficiency trade-off, improves metric and photometric consistency, and provides a practical closed-loop sensor simulation platform for E2E autonomous driving evaluation.
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