## 1 Introduction
Nevertheless, such automated analysis requires the training of the algorithm, limiting its practical applicability in low-cost devices. A similar problem is also typical for the use of neural networks, adopted e.g., for the monitoring of the 3D inkjet printing process of electronic products [20]. An automated visual inspection of the 3D printing process may also be related to collision detection and the use of visual feedback [21]. On the other hand, some more advanced systems make it possible to optimize the tool paths during manufacturing [22], as well as to use multiple filaments for fabrication [23]. However, the latter solution, known as MultiFab, requires the use of a 3D scanner and closed-feedback loop for small corrections, hence its usefulness for low-cost devices may be limited, even considering the relatively small budget provided by its authors (less than $7000). An interesting application of machine vision for the detection of defects in 3D prints has been proposed by [PERSON] [24]. This solution, based on five cameras and Raspberry Pi units, has been designed to reduce or eliminate the need for testing of printed objects due to the possibility of automatic correction of minor defects noticed during the printing process, as well as the detection of \"dry printing\" issues caused by the lack of filament. Nevertheless, the proposed system has turned out to be very sensitive to environmental conditions as well as even minor camera movements. An application of visible sensing for the detection of microdefects in the 3D printed objects used for safety-critical applications has been presented in the paper [25]. Another example of the usefulness of computer vision methods for defect inspection in plastic materials may be the surface control system based on the low-cost visual sensors designed for polyvinyl chloride (PVC) pipes, proposed recently [26]. Regardless of the great progress in the use of machine vision for the monitoring and quality assurance of the 3D printed objects, an automatic visual surface assessment is still a challenging task, particularly correlated with subjective aesthetic perception of the 3D prints. Hence, in this paper, the adaptation of some similarity-based general-purpose full-reference image quality assessment (FR IQA) methods has been proposed together with their combination and optimization towards high correlation with subjective opinions. The experiments have been conducted using a specially prepared database containing 107 images of the 3D printed flat surfaces affected by the various amounts of distortions together with subjective quality scores gathered from 92 human observers. Although in practical applications the correlation between the proposed objective quality scores and some mechanical properties would be much more desired, the measurement of some of them, e.g., strength of the materials, would require additional destructive experiments. Therefore, in this paper, we focus mainly on the aesthetic quality assessment, similarly as in general-purpose image quality assessment, based on the accordance with subjective opinions. Nevertheless, the developed methods may be further extended, particularly using the data fusion with some other non-destructive measurements, to find some dependencies between them and some physical properties of the manufactured objects. The rest of the paper is organized as follows: Section 2 contains an overview of similarity-based IQA methods, whereas the description of the developed dataset is provided in Section 3. The idea of the proposed modification of the FR IQA metrics for the quality assessment of the 3D printed surfaces and their combination (Section 4) is followed by the experimental results in Section 5. The paper ends with a discussion (Section 6) and conclusions (Section 7).