用高保真数字孪生生成真实感激光雷达数据，解决感知训练数据难问题。
UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluation
- 基于卫星图与OpenStreetMap构建真实道路环境的数字孪生
- 生成的合成数据在感知任务上超越实测数据训练的模型
- 适合智能交通系统、自动驾驶感知研究者使用
基于激光雷达的智能交通系统感知依赖大规模标注数据训练深度神经网络，但真实数据标注成本高、耗时长，制约系统扩展。Sim2Real学习提供可扩展替代方案，但其效果取决于仿真与真实环境的保真度。本文介绍一种可复现的高保真数字孪生（HiFi DT）构建流程，利用开源资源如卫星影像、OpenStreetMap及传感器参数，实现对静态几何、道路设施和动态交通的建模。所生成环境支持高效低成本的数据生成，用于鲁棒的Sim2Real学习。基于该流程，我们发布了三个合成激光雷达数据集：UT-LUMPI、UT-V2X-Real和UT-TUMTraf-I，其场景高度还原真实地点，在感知任务中表现优于实测数据训练的基线模型。本指南推动高保真数字孪生在智能交通系统研究与部署中的广泛应用。
原文摘要 · Abstract (English)
LiDAR-based perception in intelligent transportation systems (ITS) relies on deep neural networks trained with large-scale labeled datasets. However, creating such datasets is expensive, time-consuming, and labor-intensive, limiting the scalability of perception systems. Sim2Real learning offers a scalable alternative, but its success depends on the simulation's fidelity to real-world environments, dynamics, and sensors. This tutorial introduces a reproducible workflow for building high-fidelity digital twins (HiFi DTs) to generate realistic synthetic datasets. We outline practical steps for modeling static geometry, road infrastructure, and dynamic traffic using open-source resources such as satellite imagery, OpenStreetMap, and sensor specifications. The resulting environments support scalable and cost-effective data generation for robust Sim2Real learning. Using this workflow, we have released three synthetic LiDAR datasets, namely UT-LUMPI, UT-V2X-Real, and UT-TUMTraf-I, which closely replicate real locations and outperform real-data-trained baselines in perception tasks. This guide enables broader adoption of HiFi DTs in ITS research and deployment.
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