ParkingTwin: Training-Free Streaming 3D Reconstruction for Parking-Lot Digital Twins
Xinhao Liu, Yu Wang, Xiansheng Guo, Gordon Owusu Boateng, Yu Cao, Haonan Si, Xingchen Guo, Nirwan Ansari

TL;DR
ParkingTwin is a real-time, training-free system for 3D reconstruction of parking lots, leveraging open-source map data and advanced filtering to produce high-quality digital twins efficiently on low-end hardware.
Contribution
It introduces a novel, training-free approach combining OSM-based geometric construction, dynamic filtering, and illumination-robust fusion for streaming 3D reconstruction in parking lots.
Findings
Achieves 30+ FPS on entry-level GPU
Improves SSIM by 16% over previous methods
Reduces GPU memory usage by 83.3%
Abstract
High-fidelity parking-lot digital twins provide essential priors for path planning, collision checking, and perception validation in Automated Valet Parking (AVP). Yet robot-oriented reconstruction faces a trilemma: sparse forward-facing views cause weak parallax and ill-posed geometry; dynamic occlusions and extreme lighting hinder stable texture fusion; and neural rendering typically needs expensive offline optimization, violating edge-side streaming constraints. We propose ParkingTwin, a training-free, lightweight system for online streaming 3D reconstruction. First, OSM-prior-driven geometric construction uses OpenStreetMap semantic topology to directly generate a metric-consistent TSDF, replacing blind geometric search with deterministic mapping and avoiding costly optimization. Second, geometry-aware dynamic filtering employs a quad-modal constraint field (normal/height/depth…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robotic Path Planning Algorithms · Autonomous Vehicle Technology and Safety
