Generating Realistic Synthetic Data for Deep Learning

This video demonstrates an add-on for Blender that can be used to generate synthetic data for training deep learning models. The add-on simulates different types of depth sensors like LiDAR and sonar to capture point cloud data in virtual 3D environments. This allows for an automated and scalable process to create large amounts of semantically annotated training data. The add-on lets you customize sensor parameters, simulate environmental conditions, and export the data in formats suitable for deep learning applications. The synthetic data helps train models that can be deployed in the real world for tasks like autonomous driving and robotics.