CAD2Render: A Modular Toolkit for GPU-Accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

Steven Moonen, Bram Vanherle, Joris de Hoog, Taoufik Bourgana, Abdellatif Bey-Temsamani, Nick Michiels; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023, pp. 583-592

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated with our approach can perform object detection and pose estimation tasks with an high enough accuracy to direct a robot. CAD2Render will be publicly available and the link to the GitHub page will be added here.