A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces

Additive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality monitoring of the manufactured object’s surface. This makes it possible to stop the printing process if the quality is unacceptable. It helps to save the filament, energy, and time, preventing the production of items with poor aesthetic quality. In the paper, several approaches to image-based surface quality assessment are discussed and combined towards a high correlation with the subjective perception of typical quality degradations of the 3D printed surfaces, exceeding 0.9. Although one of the most significant limitations of using full-reference image quality-assessment metrics might be the lack of reference images, it can be overcome by using mutual similarity calculated for image regions. For the created dataset containing 107 samples with subjective aesthetic quality scores, it is shown that the combination of even two metrics using their weighted sum and product significantly outperforms any elementary metric or feature when considering correlations with subjective quality scores.