Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround-view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the alignment between reconstruction and downstream parking slot detection, we introduce a slot-aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state-of-the-art reconstruction quality and better preserves perception consistency for downstream tasks. The code and dataset will be released at: https://github.com/wm-research/ParkGaussian

翻译：泊车是自动驾驶系统（ADS）的一项关键任务，在拥挤泊车位与GPS拒止环境中面临独特挑战。然而，现有研究多集中于二维泊车位感知、建图与定位，对三维重建的探索仍显不足，而三维重建对于捕捉泊车场景中复杂空间几何结构至关重要。单纯提升重建泊车场景的视觉质量并不能直接惠及自主泊车，因为泊车任务的关键入口在于车位感知模块。为应对这些局限，我们构建了首个专为泊车场景重建设计的基准数据集ParkRecon3D，其中包含四路环视鱼眼相机的传感器数据（已标定外参）以及密集的泊车位标注。在此基础上，我们提出ParkGaussian——首个集成3D高斯溅射（3DGS）技术用于泊车场景重建的框架。为进一步提升重建结果与下游泊车位检测任务的对齐度，我们引入了一种车位感知重建策略，该策略利用现有泊车感知方法以增强车位区域的合成质量。在ParkRecon3D数据集上的实验表明，ParkGaussian实现了最先进的重建质量，并更好地保持了面向下游任务的感知一致性。代码与数据集将于以下地址发布：https://github.com/wm-research/ParkGaussian