Matheus Ferreira Coelho Pinho

Purpose: This study evaluates the reliability of three-dimensional (3D) facial measurements obtained from different acquisition technologies. Specifically, it investigates whether low-cost smartphone-based photogrammetry and videogrammetry can produce measurements comparable to those derived from structured light scanning, and identifies sources of variability associated with each modality. Methods: Fifteen participants were imaged using three techniques: structured light scanning, smartphone photogrammetry, and smartphone videogrammetry. Inter-landmark distances were computed for each resulting 3D model, including both stable anatomical landmarks and those prone to placement difficulty. A statistical workflow was applied, incorporating non-parametric hypothesis testing, iterative outlier detection, and normality assessments to evaluate measurement variability attributable to acquisition modality, subject movement, and landmark uncertainty. Results: The three acquisition modalities produced broadly comparable inter-landmark measurements, indicating no substantial loss of accuracy when using smartphone-based methods. However, increased variability was observed in anatomically complex regions, particularly the perioral area, as well as in landmarks known to be difficult to localize. These discrepancies persisted across all modalities, including structured light scanning. Conclusions: Smartphone-based photogrammetry and videogrammetry represent viable, low-cost alternatives to structured light scanning for 3D facial measurement. Nonetheless, landmark-specific uncertainty remains a critical factor, particularly in challenging anatomical regions. The findings highlight the importance of considering both acquisition modality and landmark reliability in the design and reporting of facial anthropometric studies.

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction.

Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the precision needed for accurate assessments, highlighting the appeal of imaging technologies like structured light scanners and photogrammetric systems. However, high-end commercial systems remain cost prohibitive, especially for public health services in developing countries. This study aims to evaluate cell-phone-based photogrammetric methods for generating 3D facial models to detect facial asymmetries. For this purpose, 15 patients had their faces scanned with the ACADEMIA 50 3D scanner, as well as with cell phone images and videos using photogrammetry and videogrammetry, resulting in 3D facial models. Each 3D model (coming from a 3D scanner, photogrammetry, and videogrammetry) was half-mirrored to analyze dissimilarities between the two ideally symmetric face sides using Hausdorff distances between the two half-meshes. These distances were statistically analyzed through various measures and hypothesis tests. The results indicate that, in most cases, both photogrammetric and videogrammetric approaches are as reliable as 3D scanning for detecting facial asymmetries. The benefits and limitations of using images, videos, and 3D scanning are also presented.