DecoupleGS: エンドツーエンド自動運転テストのためのインタラクティブ3Dガウススプラッティング
DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing
エンドツーエンド自動運転の検証用に、高忠実度の静的背景と操作可能な動的エージェントを分離した3Dガウススプラッティングフレームワークを提案し、リアルタイムでインタラクティブなクローズドループシミュレーションを実現した。
著者: Siying Li, Ying Ni, Jie Sun, Jian Sun, Haotian Shi
分類: cs.CV
原文アブストラクト
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.