Sensor Simulation

Sensor simulation aims to create realistic virtual sensor data, enabling algorithm development and testing without the cost and limitations of real-world experiments. Current research emphasizes physically-based models, particularly for LiDAR and camera data, often incorporating techniques like ray tracing, neural radiance fields (NeRFs), and generative adversarial networks (GANs) to enhance realism. This work is crucial for advancing autonomous systems, robotics, and healthcare applications by providing large, diverse, and controlled datasets for training and evaluating algorithms, ultimately bridging the "sim2real" gap and improving system robustness.