Photogrammetric Techniques for Paleoanthropological Objects Preserving and Studying

Knyaz, V. A.; Leybova, N. A.; Galeev, R. M.; Novikov, M. M.; Gaboutchian, A. V.

doi:10.5194/isprs-archives-xlii-2-525-2018

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Due to this flexibility, SFM photogrammetry has been widely used in 3D data acquisition for geographical and geological surveys, architectural preservation, and 3D modeling for archeological and paleontological sites (Wang et al, 2019; Westoby et al, 2012; Zimmer et al, 2018). In recent years, SFM photogrammetry has also been introduced to biological data collection for research, museum archives, and anatomical education as a flexible and low-cost tool (Giacomini et al, 2019; Knyaz et al, 2018; Lauria et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Most previous studies that evaluated SFM photogrammetry for biological research used commercial software, for which the costs might be substantial or yearly accruing (Fahlke & Autenrieth, 2016; Giacomini et al, 2019; Knyaz et al, 2018; Waltenberger et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An open-source photogrammetry workflow for reconstructing 3D models

Zhang

Maga

2023

Preprint

View full text Add to dashboard Cite

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“…2019 ). In recent years, SFM photogrammetry has also been introduced to biological data collection for research, museum archives, and anatomical education as a flexible and low-cost tool ( Knyaz et al. 2018 ; Giacomini et al.…”

Section: Introductionmentioning

confidence: 99%

“…Most previous studies that evaluated SFM photogrammetry for biological research used commercial software, for which the costs might be substantial or yearly accruing ( Fahlke & Autenrieth 2016 ; Knyaz et al. 2018 ; Giacomini et al.…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Photogrammetry Workflow for Reconstructing 3D Models

Zhang

Maga

2023

Integrative Organismal Biology

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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 relied on commercial software to reconstruct the 3D model from photographs, which impeded 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 presented 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 assessed 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 comprised fifteen Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results showed that the geometry of the models derived from ODM was sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captured consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models could affect the landmark placement, which in return affected 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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