无需训练，实时重建停车场3D数字孪生，速度快且内存低。
ParkingTwin: Training-Free Streaming 3D Reconstruction for Parking-Lot Digital Twins
- 利用地图语义拓扑生成精确几何结构，跳过耗时优化。
- 实时过滤动态物体和临时遮挡，保持场景稳定。
- 抗光照变化融合，适合真实复杂环境，兼容游戏引擎。
高保真停车场数字孪生为自动代客泊车中的路径规划、碰撞检测与感知验证提供关键先验。然而，面向机器人重建面临三重困境：前向视图稀疏导致视差弱、几何病态；动态遮挡与极端光照影响纹理融合稳定性；神经渲染通常需昂贵离线优化，违反边缘端流式处理约束。本文提出ParkingTwin，一种无需训练的轻量级在线3D重建系统。首先，基于OSM语义拓扑的几何构建方法直接生成符合度量一致性的TSDF，以确定性映射替代盲搜，避免代价高昂的优化。其次，采用四模态约束场（法线/高度/深度一致性）进行几何感知动态过滤，实时剔除移动车辆与瞬态遮挡。第三，在CIELAB空间通过自适应L通道加权与深度梯度抑制实现光照鲁棒融合，显著减少突变光照下的接缝。ParkingTwin在入门级GTX 1660上实现30+ FPS。在68,000平方米真实数据集上，达到SSIM 0.87（提升16.0%），相比主流3D高斯泼溅（3DGS）实现约15倍端到端加速，且显存降低83.3%。系统输出可直接接入Unity/Unreal数字孪生管线的显式三角网格。
原文摘要 · Abstract (English)
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 consistency) to reject moving vehicles and transient occlusions in real time. Third, illumination-robust fusion in CIELAB decouples luminance and chromaticity via adaptive L-channel weighting and depth-gradient suppression, reducing seams under abrupt lighting changes. ParkingTwin runs at 30+ FPS on an entry-level GTX 1660. On a 68,000 m^2 real-world dataset, it achieves SSIM 0.87 (+16.0%), delivers about 15x end-to-end speedup, and reduces GPU memory by 83.3% compared with state-of-the-art 3D Gaussian Splatting (3DGS) that typically requires high-end GPUs (RTX 4090D). The system outputs explicit triangle meshes compatible with Unity/Unreal digital-twin pipelines. Project page: https://mihoutao-liu.github.io/ParkingTwin/
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