A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone Scans with Deep Learning Based Methods Using Geometry and Morphometry Criteria
Abstract
Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications. However, the high cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods. This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques, providing a statistical framework for assessing facial morphology preservation. As a case study, we compare smartphone-based 3D scans with state-of-the-art deep learning reconstruction methods from 2D images, using high-end stereophotogrammetry models as ground truth. This methodology enables a quantitative assessment of global and local shape differences, offering a biologically meaningful validation approach for low-cost 3D facial acquisition and reconstruction techniques.
Cite
@article{arxiv.2502.09425,
title = {A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone Scans with Deep Learning Based Methods Using Geometry and Morphometry Criteria},
author = {Álvaro Heredia-Lidón and Alejandro Moñux-Bernal and Alejandro González and Luis M. Echeverry-Quiceno and Max Rubert and Aroa Casado and María Esther Esteban and Mireia Andreu-Montoriol and Susanna Gallardo and Cristina Ruffo and Neus Martínez-Abadías and Xavier Sevillano},
journal= {arXiv preprint arXiv:2502.09425},
year = {2025}
}