用高斯点阵实现车路协同场景的高保真重建与灵活编辑
CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting
- 分解动态物体为可编辑的高斯表示，支持场景自由修改
- 在真实车路协同数据上重建精度高，提升多视角3D检测性能
- 适合自动驾驶数据增强与极端场景生成，尤其适用于测试系统鲁棒性
车路协同（V2X）通信在自动驾驶中至关重要，促进车辆与基础设施间的协作。尽管仿真在自动驾驶任务中已有广泛应用，但在车路协同场景下的数据生成与扩充潜力仍未充分挖掘。本文提出CRUISE，一个面向车路协同驾驶环境的综合性重建与合成框架。该框架采用分解式高斯点阵，精确重建真实世界场景，并支持灵活编辑。通过将动态交通参与者分解为可编辑的高斯表示，实现驾驶场景的无缝修改与增强。同时，框架可从本车和基础设施视角渲染图像，实现大规模车路协同数据集扩充，用于训练与评估。实验表明：1）CRUISE能以高保真度重建真实车路协同驾驶场景；2）使用CRUISE可提升本车、基础设施及协同视角下的3D检测性能，以及在V2X-Seq基准上的协同3D跟踪效果；3）能有效生成具有挑战性的边缘案例。
原文摘要 · Abstract (English)
Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly contributed to various autonomous driving tasks, its potential for data generation and augmentation in V2X scenarios remains underexplored. In this paper, we introduce CRUISE, a comprehensive reconstruction-and-synthesis framework designed for V2X driving environments. CRUISE employs decomposed Gaussian Splatting to accurately reconstruct real-world scenes while supporting flexible editing. By decomposing dynamic traffic participants into editable Gaussian representations, CRUISE allows for seamless modification and augmentation of driving scenes. Furthermore, the framework renders images from both ego-vehicle and infrastructure views, enabling large-scale V2X dataset augmentation for training and evaluation. Our experimental results demonstrate that: 1) CRUISE reconstructs real-world V2X driving scenes with high fidelity; 2) using CRUISE improves 3D detection across ego-vehicle, infrastructure, and cooperative views, as well as cooperative 3D tracking on the V2X-Seq benchmark; and 3) CRUISE effectively generates challenging corner cases.
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