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Related Experiment Video

Updated: Jul 10, 2026

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Craniofacial 3D Morphometric Analysis with Smartphone-Based Photogrammetry.

Omar C Quispe-Enriquez¹, Juan José Valero-Lanzuela¹, José Luis Lerma¹

¹Photogrammetry and Laser Scanner Research Group (GIFLE), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

Smartphone photogrammetry accurately captures craniofacial 3D models for medical use. This method offers an accessible alternative for creating detailed head 3D models, crucial for surgical planning and research.

Keywords:

3D model 3D scanning cranial facial photogrammetry smartphone device

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Area of Science:

Medical Imaging
Biotechnology
Computer Science

Background:

3D craniofacial morphometric data is vital for medical and educational fields.
Accurate 3D head models are essential for applications like surgical planning.

Purpose of the Study:

To evaluate smartphone-based photogrammetry for creating accurate craniofacial 3D models.
To compare the efficacy of using photos versus video for 3D model generation.

Main Methods:

Acquired craniofacial 3D models using a Samsung Galaxy S22 smartphone on volunteers and mannequins.
Processed data using Agisoft Metashape and PhotoMeDAS software.
Validated results against a high-precision white-light scanner (Academia 50).

Main Results:

Photogrammetry with photos yielded a mesh accuracy of 0.22 ± 1.29 mm.
Videogrammetry with video frames achieved 0.47 ± 1.43 mm accuracy.
PhotoMeDAS software showed 0.39 ± 1.02 mm accuracy, with linear measurements at 0.75 mm (photogrammetry), 1 mm (videogrammetry), and 1.25 mm (PhotoMeDAS).

Conclusions:

Smartphone photogrammetry, using either photos or video, provides a viable method for obtaining craniofacial 3D models.
This technique has significant potential applications in neurosurgery and maxillofacial surgery.
The study highlights the accessibility and accuracy of smartphone-based 3D craniofacial data acquisition.