ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking
This work addresses a critical need for 3D scene reconstruction in autonomous driving systems, specifically for parking in crowded and GPS-denied environments, though it is incremental as it builds on existing 3D Gaussian Splatting methods.
The paper tackles the problem of 3D reconstruction for autonomous parking scenes, which is underexplored compared to 2D methods, by proposing ParkGaussian, a framework that integrates 3D Gaussian Splatting and achieves state-of-the-art reconstruction quality and better perception consistency for downstream tasks.
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