Photogrammetry open-source tools
Nagar, Sahar (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023051610987
https://urn.fi/URN:NBN:fi:amk-2023051610987
Tiivistelmä
The subject of this thesis is the exploration of open-source photogrammetry tools, thus emphasizing their ethical considerations, comparisons with commercial software, advantages, use cases, cross-platform compatibility, challenges, and future innovations. The shift towards open-source tools is due to their cost-effectiveness, flexibility, and community support.
The research was conducted using a combination of literature review and practical experimentation to evaluate and compare various open source photogrammetry tools. Ethical considerations were identified and discussed, as were the advantages and disadvantages of each tool. The major findings include the cost-effectiveness and versatility of open source tools, as well as their potential for cross-platform compatibility. In particular, the research identified the self-reporting and customization capabilities of Blender's Python scripts as a significant advantage for users seeking to tailor their photogrammetry workflow.
One of the key challenges identified was data quality, which can affect the accuracy of 3D models created using photogrammetry. Future innovations, such as neural radiance field (NERF) and fine 3D modeling technology, were explored as potential solutions to this issue.
Overall, open-source photogrammetry tools offer many advantages over commercial solutions, and their ethical considerations, use cases, and cross-platform compatibility make them a compelling option for creating 3D models. Thus, the use of open-source photogrammetry tools is likely to continue to grow in popularity.
The research was conducted using a combination of literature review and practical experimentation to evaluate and compare various open source photogrammetry tools. Ethical considerations were identified and discussed, as were the advantages and disadvantages of each tool. The major findings include the cost-effectiveness and versatility of open source tools, as well as their potential for cross-platform compatibility. In particular, the research identified the self-reporting and customization capabilities of Blender's Python scripts as a significant advantage for users seeking to tailor their photogrammetry workflow.
One of the key challenges identified was data quality, which can affect the accuracy of 3D models created using photogrammetry. Future innovations, such as neural radiance field (NERF) and fine 3D modeling technology, were explored as potential solutions to this issue.
Overall, open-source photogrammetry tools offer many advantages over commercial solutions, and their ethical considerations, use cases, and cross-platform compatibility make them a compelling option for creating 3D models. Thus, the use of open-source photogrammetry tools is likely to continue to grow in popularity.