What Does 2D Geometric Information Really Tell Us About 3D Face Shape?

The accuracy of a three-dimensional face model reconstructing method based on conventional clinical two-dimensional photos

Background

This study aims to investigate the accuracy of a three-dimensional (3D) face reconstruction method based on conventional clinical two-dimensional (2D) photos.

Methods

Twenty-three patients were included, and Character Creator v3.2 software with the Headshot v1.0 plugin was used for 3D face model reconstruction. Various facial landmarks were finely adjusted manually to refine the models. After preprocessing and repositioning, 3D deviation analysis was performed. The accuracy of the landmarks in different dimensions was determined, and twelve facial soft tissue measurements were compared to validate the clinical potential of the method.

Result

The reconstructed 3D face models showed good facial morphology with fine texture. The average root mean square errors between face scan models and reconstructed models at perioral area (1.26 ± 0.24 mm, 95%CI: 1.15–1.37 mm) were significantly smaller than the entire facial area (1.77 ± 0.23 mm, 95%CI:1.67–1.88 mm), P < 0.01. The deviation of menton of soft tissue was significantly larger than pronasale (P < 0.01). The deviations of all landmarks in the Y-direction were significantly larger than those in the other 2 dimensions (Y > Z > X, P < 0.01). A significant difference (P < 0.05) of approximately 1.5 mm was found for facial height. Significant differences (P < 0.05) were also identified in the remaining 6 soft tissue measurements, with average deviations no greater than 0.5 mm (linear measurement) or 1.2° (angular measurements).

Conclusion

A 3D face modeling method based on 2D face photos was revealed and validated. The reconstruction accuracy of this method is clinically acceptable for orthodontic measurement purposes, but narrow clinical indications and labor-intensive operations remain problems.

scAnt-an open-source platform for the creation of 3D models of arthropods (and other small objects)

We present scAnt, an open-source platform for the creation of digital 3D models of arthropods and small objects. scAnt consists of a scanner and a Graphical User Interface, and enables the automated generation of Extended Depth Of Field images from multiple perspectives. These images are then masked with a novel automatic routine which combines random forest-based edge-detection, adaptive thresholding and connected component labelling. The masked images can then be processed further with a photogrammetry software package of choice, including open-source options such as Meshroom, to create high-quality, textured 3D models. We demonstrate how these 3D models can be rigged to enable realistic digital specimen posing, and introduce a novel simple yet effective method to include semi-realistic representations of approximately planar and transparent structures such as wings. As a result of the exclusive reliance on generic hardware components, rapid prototyping and open-source software, scAnt costs only a fraction of available comparable systems. The resulting accessibility of scAnt will (i) drive the development of novel and powerful methods for machine learning-driven behavioural studies, leveraging synthetic data; (ii) increase accuracy in comparative morphometric studies as well as extend the available parameter space with area and volume measurements; (iii) inspire novel forms of outreach; and (iv) aid in the digitisation efforts currently underway in several major natural history collections.