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 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.