Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry

Matuzevičius, Dalius; Serackis, Artūras

doi:10.3390/app12010229

Cited by 20 publications

(13 citation statements)

References 68 publications

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“…The second most popular field is medical applications. Solutions that allow recreating the shape and size of individual parts of the body [ 45 , 46 , 47 , 57 , 58 ] and models for the needs of, e.g., prostheses or plastic surgeries [ 48 , 49 , 51 , 63 ] are predominant here. In the case of geological issues, the analysis of rock porosity and roughness is a common topic [ 65 , 66 ], while in geomorphology it is popular to create digital models of various types of geomorphological forms (e.g., cliffs, caves) [ 70 , 71 ].…”

Section: Introductionmentioning

confidence: 99%

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

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Recently, the term smartphone photogrammetry gained popularity. This suggests that photogrammetry may become a simple measurement tool by virtually every smartphone user. The research was undertaken to clarify whether it is appropriate to use the Structure from Motion—Multi Stereo View (SfM-MVS) procedure with self-calibration as it is done in Uncrewed Aerial Vehicle photogrammetry. First, the geometric stability of smartphone cameras was tested. Fourteen smartphones were calibrated on the checkerboard test field. The process was repeated multiple times. These observations were found: (1) most smartphone cameras have lower stability of the internal orientation parameters than a Digital Single-Lens Reflex (DSLR) camera, and (2) the principal distance and position of the principal point are constantly changing. Then, based on images from two selected smartphones, 3D models of a small sculpture were developed. The SfM-MVS method was used, with self-calibration and pre-calibration variants. By comparing the resultant models with the reference DSLR-created model it was shown that introducing calibration obtained in the test field instead of self-calibration improves the geometry of 3D models. In particular, deformations of local concavities and convexities decreased. In conclusion, there is real potential in smartphone photogrammetry, but it also has its limits.

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Section: Introductionmentioning

confidence: 99%

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

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“…Reducing background noise is critical to ensure successful photogrammetric reconstruction and high-quality textured modelling (Matuzevičius & Serackis, 2021). Simply covering the background with the same color is helpful but insufficient.…”

Section: Discussionmentioning

confidence: 99%

An open-source photogrammetry workflow for reconstructing 3D models

Zhang

Maga

2023

Preprint

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Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still rely on commercial software to reconstruct the 3D model from photographs, which impedes the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we present an affordable and highly flexible structure-from-motion photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assess the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprises fifteen Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results show that the geometry of the models derived from ODM is sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captures consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models can affect the landmark placement, which in return affect the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.

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“…3D models of organs or parts of the human body are helpful in various medical specializations and other research fields. Created with the help of a smartphone, they are presented during the reconstruction of the whole head [10], face [11], foot [12], analysis of the cranial deformation [13], and in dental monitoring [14].…”

Section: Introductionmentioning

confidence: 99%

Unexpected corneal reflection phenomenon alters smartphone 3D image-based models of the eye

Kurilová,

Bemberáková,

Kocián

et al. 2023

Journal of Electrical Engineering

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Reconstruction of a 3D eye model by photogrammetry from a smartphone video could be prospectively used in self-diagnosis, screening and telemedicine monitoring of diseases of the front part of the eye and its surroundings. The main use could be found in the treatment of diseases of the curvature and surface of the cornea and in follow-up after some refractive procedures. In our work, we create 3D image-based models of the eye after scanning the face with a smartphone. An unexpected phenomenon appeared during the reconstruction of the transparent cornea – a crater-like depression was formed at the place where nearby objects reflected on the cornea, which corresponds to the first Purkinje image, the so-called glint. We thus encountered complications that may arise when modelling transparent living structures from a video taken in a normal environment, which will need to be solved if we want to create such 3D models of the eye using this method for medical purposes. Another 3D reconstruction approach or additional algorithms must be considered as a future work.

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Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry

Cited by 20 publications

References 68 publications

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

An open-source photogrammetry workflow for reconstructing 3D models

Unexpected corneal reflection phenomenon alters smartphone 3D image-based models of the eye

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