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 by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.
翻译:在产品和组装质量控制方面,计算机视野的使用在制造业中正在变得司空见惯。最近,基于机器学习的解决方案显然在性能和稳健性方面优于经典计算机视觉算法。然而,一个主要的缺点是,它们需要足够大和贴标签的培训数据集,而这些数据集往往不具备,或过于繁琐,而且过于耗时,难以获取。对于低容量和高差异的制造业来说尤其如此。幸运的是,在这个行业中,制造或组装产品的CAD模型已经可供使用。本文介绍了基于United High Dender Pipline(HDRRRP)的GAD2Recle-加速合成数据生成器。CAD2ReADReander设计了一种模块式的变异,使得能够根据手头工业使用案例的需要定制高的数据生成。虽然CAD2Reander是专门设计用于制造用途案例的,但也可以用于其他领域。我们验证CAD2Reander,方法是在两个工业相关数据集中演示艺术性表现的状态。我们展示了一种高精确度的CADDAD/ROAD方法。我们用来在CADDDAD中进行高的测试时,可以使用一个高的模型。