论文标题
CAD2RENDER：用于制造业的GPU加速光真实数据生成的模块化工具包
CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry
论文作者
论文摘要
在制造业中，将计算机视觉用于产品和组装质量控制正在变得无处不在。最近，很明显，基于机器的解决方案在性能和鲁棒性方面表现优于经典的计算机视觉算法。但是，主要的缺点是它们需要足够大的培训数据集，这些数据集通常是不可用的或太乏味的，而且太耗时而无法获取。对于低体积和高空制制造而言，尤其如此。幸运的是，在这个行业中，可以使用制造或组装产品的CAD模型。本文介绍了基于Unity高清渲染管道（HDRP）的GPU加速综合数据生成器Cad2Render。 CAD2Render旨在以模块化的方式增加变化，从而使高可自定义的数据生成可以根据手头工业用例的需求进行量身定制。尽管CAD2Render是专门为制造用例设计的，但也可以用于其他域。我们通过在两个工业相关的设置中证明最先进的表现来验证CAD2Render。我们证明，我们的方法生成的数据可用于训练对象检测，并具有足够准确性的估计模型以指导机器人。 Cad2Render的代码可在https://github.com/edm-research/cad2render上获得。
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.
