UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluation
This enables scalable and cost-effective data generation for Sim2Real learning in ITS, though it is incremental as it builds on existing simulation methods.
The paper tackles the problem of expensive and labor-intensive LiDAR dataset creation for intelligent transportation systems by introducing a reproducible workflow for building high-fidelity digital twins to generate realistic synthetic datasets, resulting in three synthetic LiDAR datasets that outperform real-data-trained baselines in perception tasks.
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.