DrivAerNet++: Renderings

In addition to the 3D mesh data, our dataset includes high-fidelity photorealistic renderings of automotive designs generated through advanced diffusion models using Stable Diffusion and ControlNet architectures. These renderings provide visually compelling, camera-ready representations that bridge the gap between technical 3D geometry and real-world automotive photography. The ControlNet-guided generation ensures structural consistency with the underlying vehicle geometries while enabling diverse lighting conditions, materials, and environmental contexts. These photorealistic renderings are instrumental for a range of machine learning tasks, including image-to-3D reconstruction, domain adaptation, style transfer, synthetic data augmentation, and multi-modal representation learning. The high-quality visual representations can also facilitate automated design evaluation workflows by providing realistic visual feedback for design iterations and enabling human-in-the-loop design processes. By incorporating these photorealistic renderings alongside technical mesh data, our dataset enhances the utility for developing and testing advanced algorithms in automotive design visualization, computer graphics, and AI-assisted design workflows. Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.