IntDT: A comprehensive roadside dual-lidar dataset and benchmarks for urban intersection digital twins

The development of digital twins for urban road intersections has attracted significant attention in recent research, driven by their potential to enable intelligent intersections that require accurate and real-time monitoring of dynamic environments. Lidar sensors, with their precise 3D perception capabilities, are particularly well-suited for enhancing intersection perception, offering accurate detection and tracking of road objects, as well as precise velocity estimation. However, existing roadside lidar datasets are deficient in terms of scene diversity and annotation information specifically tailored for intersection monitoring applications. To address the gap, we introduce IntDT, a comprehensive roadside lidar dataset designed specifically for digital twins of intersections. Collected using dual roadside lidar systems across diverse urban intersections, IntDT provides comprehensive annotations that support a range of tasks. Benchmark evaluations are performed for 3D object detection and tracking tasks to facilitate standardized comparative analysis. Additionally, semi-supervised object detection, moving object segmentation, and point cloud completion tasks are firstly benchmarked for roadside lidar data to stimulate more studies in this field. The dataset and code will be released at https://github.com/Geo3DSmart/IntDT . • Present a large-scale roadside lidar dataset for digital twins of intersections. • Provide 3D box annotations, tracking IDs, and motion states for multiple tasks. • Establish benchmarks for object detection, SSL detection, tracking, MOS, and PCC.