LiDAR Simulation

LiDAR simulation aims to generate realistic LiDAR point cloud data for training and testing autonomous driving and other 3D perception systems, overcoming the limitations and high cost of real-world data acquisition. Current research focuses on developing efficient and high-fidelity simulation methods, employing techniques like Gaussian splatting, neural radiance fields (NeRFs), generative adversarial networks (GANs), and various neural field approaches to accurately model sensor characteristics and scene interactions, including dynamic objects and varying point densities. These advancements are crucial for accelerating the development and validation of perception algorithms, particularly in applications where real-world data collection is challenging or expensive, such as forestry or railway environments.