Automated visual quality assessment for virtual and augmented reality based digital twins

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.