The field of LiDAR-based perception in Intelligent Transportation Systems (ITS) is moving towards the adoption of digital twins as a solution to the Sim2Real gap. Digital twins are being used to create high-fidelity synthetic datasets that can replace or augment real-world datasets, enabling scalable and cost-effective training of deep learning models. This approach has been shown to improve the generalization of perception models across diverse evaluation scenarios. The use of digital twins is also enabling the creation of realistic replicas of roadside LiDAR datasets, which can be used to train models on tasks such as 3D object detection, tracking, and semantic and instance segmentation. Noteworthy papers in this area include PercepTwin, which introduces a rigorous methodology for creating large-scale, high-quality synthetic datasets using digital twins. High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception proposes a framework that incorporates real-world background geometry, lane-level road topology, and sensor-specific specifications and placement, and demonstrates improved performance over models trained on real data. UrbanTwin presents high-fidelity synthetic replicas of public roadside LiDAR datasets, which can be used to train deep learning models and improve detection performance. SynthDrive proposes a scalable real2sim2real system for high-fidelity asset generation and driving data synthesis.