DecoupleGS：用于端到端自动驾驶测试的交互式三维高斯溅射
DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing
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中文总结 AI 辅助
DecoupleGS是一种解耦三维高斯溅射框架，通过分解场景为静态背景与动态智能体，结合三个针对性模块实现高保真交互，为端到端自动驾驶测试提供实用闭环传感器仿真平台。
中文摘要 AI 辅助
端到端（E2E）自动驾驶算法需要在具备高视觉保真度、强交互性和实时性能的仿真环境中进行严格的闭环验证。现有方法，从游戏引擎到静态神经渲染，在这些需求之间存在固有权衡，且难以满足E2E测试必需的动态场景构建需求。为填补这一空白，我们提出了一种专为大规模E2E评估设计的新型解耦三维高斯溅射（3DGS）框架。我们以面向对象的规范表示法将场景根本性地分解为高保真静态背景和可操控动态智能体。为解决由此产生的表示冲突，我们引入了三个针对性模块：（1）通过感知剪枝和矢量量化实现的资产压缩，用于实时交通渲染；（2）利用语义拓扑实现的地图引导几何配准，以严格对齐轨迹；（3）基于代理的重光照，用于传递环境光照以实现无缝光度集成。大量实验表明，DecoupleGS实现了保真度与效率的平衡权衡，提升了度量与光度一致性，并为E2E自动驾驶评估提供了实用的闭环传感器仿真平台。
英文摘要
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.
发表机构
- College of Transportation, Tongji University（同济大学交通运输学院）
- Key Laboratory of Road and Traffic Engineering, Ministry of Education（教育部道路与交通工程重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
