Estimation of 3D shape and volume of fire plumes from multiple views
This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method’s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semi-transparent smoke without p...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/427442 |
| Acceso en línea: | https://hdl.handle.net/2117/427442 https://dx.doi.org/10.1088/1742-6596/2885/1/012075 |
| Access Level: | acceso abierto |
| Palabra clave: | Calibration Cameras Deep learning Fires Image reconstruction Smoke Wildfires Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method’s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semi-transparent smoke without precise calibration. The research emphasizes the significance of pre-processing and data appearance for neural network efficacy. By improving 3D reconstruction techniques, this work aids in advancing wildfire tracking and environmental analysis, offering a practical approach for real-world applications in fire science. |
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