acquiring real-world Lidar data for training and testing these models is costly and complex. This project uses simulation environments, specifically the CARLA simulator built on Unreal Engine, to generate synthetic Lidar data and ground truth labels for 3D object detection tasks. The goal is to use simulated data to fine-tune an already trained model for Lidar-based object detection. By simulating various conditions and sensor configurations in CARLA, we aim to capture rich Lidar data along with 3D bounding boxes for cars and pedestrians. This work will evaluate the impact of simulated Lidar data on model performance, comparing results before and after fine-tuning the model with our synthetic dataset.