This paper describes a new synthetic data generation pipeline called 3DGENie designed to generate 3D point clouds to train deep learning computer vision models. 3DGENie uses procedural layout generation to produce region layout trees. It then applies 3D scene construction and asset randomization to produce scenes populated with 3D assets. Synthetic sensors are placed in the virtual environment to simulate data capture from the 3D scenes as if monitored by real-world sensors. 3DGENie uses Nvidia Omniverse as its scene building platform and Pixar’s Universal Scene Description (USD) for 3D graphics representation to allow for seamless interchange across platforms. Our main application focuses on the generation of industrial car assembly lines, yet 3DGENie can be used across different applications. We conduct experiments to evaluate the generated 3D point clouds, using several deep learning semantic segmentation models. Results highlight the quality of our pipeline.