Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($<100$ models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories. This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$, and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.

中文翻译：

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区分伪像：评估卷积神经网络的饱和点

先前的工作表明，使用替代计算机辅助设计（CAD）模型训练的卷积神经网络（CNN）能够从照片中检测和分类真实世界的伪像。其应用程序支持设计中数字和物理资产的孪生，包括从模型存储库中快速提取零件几何形状，信息搜索和检索以及在现场识别组件以进行维护，维修和记录。已经显示出CNN在分类任务中的表现取决于训练数据集的大小和班级数量。在先前的工作使用相对较小的替代模型数据集（$ <100 $模型）的情况下，CNN在越来越大的模型存储库中区分模型的能力仍然存在问题。本文提出了一种从在线CAD模型库中生成合成图像数据集的方法，并进一步研究了在合成数据上训练的现成CNN体系结构随类规模增加而对模型进行分类的能力。策划和处理了1,000个CAD模型，以生成大规模的代理数据集，其模型覆盖率分别为10 $ ^ {\ circ} $，30 $ ^ {\ circ} $，60 $ ^ {\ circ} $和120 $ ^ {\ circ} $度。研究结果表明，计算机视觉算法能够对多达200个模型库中的伪像进行分类，超过这一点，就可以观察到CNN的性能显着下降，从而限制了其目前对物理伪像与数字伪像自动配对的能力。虽然，