UT-TUMTraf-I

UCF UrbanTwin (UT) dataverse is a collection of high-fidelity synthetic roadside lidar datasets. Each dataset in this dataverse supports core perception tasks in intelligent transportation systems, including 3D object detection, multi-object tracking, semantic segmentation, and instance segmentation. Generated through precise simulation of real-world intersections and sensor settings, these datasets exhibit minimal sim-to-real domain gap and enable the training of deep learning models that perform competitively on real data benchmarks. This synthetic data replicates subset R2 sequence 03 of the original TUMTraf-I dataset, containing labeled intersection lidar data. It is designed to mirror the physical layout of the target location, Garching bei München intersection in Germany. The sensors are also modeled after the sensor specifications of the original real dataset. The dataset contains ~50K points and 9 road-user objects per frame in an 80x80x10 meter^3 region roughly centered at the intersection area, all matching closely to the original dataset.