Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.

中文翻译：

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基于虚拟和增强现实的数字孪生的自动视觉质量评估

虚拟和增强现实数字孪生在许多行业中变得越来越普遍，尽管数字孪生系统应用程序的生产对于许多小型组织来说仍然昂贵得令人望而却步。降低数字孪生成本的关键一步在于自动化 3D 资产的生产，但由于缺乏合适的自动化方法来确定这些资产的视觉质量，工作变得复杂。虽然视觉质量评估多年来一直是一个活跃的研究领域，但很少有出版物在数字孪生资产创建的背景下考虑这一过程。在这项工作中，我们引入了一种使用机器学习的自动抽取程序来评估抽取的视觉影响，这是一种常用于 3D 资产生产的过程，迄今为止在视觉评估文献中尚未得到充分体现。我们的模型结合了 108 个几何和感知指标来确定 3D 对象在抽取过程中是否发生了不可接受的扭曲。我们的模型在近 4, 000 个扭曲网格上进行训练，比文献中的许多模型具有更广泛的适用性。我们的结果显示，针对一组测试模型的精度超过 97%，性能测试表明我们的模型能够在 2 分钟内对多达 25, 000 个多边形的模型执行评估。基于这些结果，我们相信我们的模型不仅在视觉质量评估领域取得了重大进步，而且在降低基于虚拟和增强现实的数字孪生成本方面迈出了重要一步。