Surface Normal Reconstruction Using Polarization-Unet
<|reference_start|>Surface Normal Reconstruction Using Polarization-Unet: Today, three-dimensional reconstruction of objects has many applications in various fields, and therefore, choosing a suitable method for high resolution three-dimensional reconstruction is an important issue and displaying high-level details in three-dimensional models is a serious challenge in this field. Until now, active methods have been used for high-resolution three-dimensional reconstruction. But the problem of active three-dimensional reconstruction methods is that they require a light source close to the object. Shape from polarization (SfP) is one of the best solutions for high-resolution three-dimensional reconstruction of objects, which is a passive method and does not have the drawbacks of active methods. The changes in polarization of the reflected light from an object can be analyzed by using a polarization camera or locating polarizing filter in front of the digital camera and rotating the filter. Using this information, the surface normal can be reconstructed with high accuracy, which will lead to local reconstruction of the surface details. In this paper, an end-to-end deep learning approach has been presented to produce the surface normal of objects. In this method a benchmark dataset has been used to train the neural network and evaluate the results. The results have been evaluated quantitatively and qualitatively by other methods and under different lighting conditions. The MAE value (Mean-Angular-Error) has been used for results evaluation. The evaluations showed that the proposed method could accurately reconstruct the surface normal of objects with the lowest MAE value which is equal to 18.06 degree on the whole dataset, in comparison to previous physics-based methods which are between 41.44 and 49.03 degree.<|reference_end|>
@article{mortazavi2024surface,
    title={Surface Normal Reconstruction Using Polarization-Unet},
    author={F. S. Mortazavi, S. Dajkhosh, and M. Saadatseresht},
    journal={arXiv preprint arXiv:2406.15118},
    year={2024},
    doi={10.5194/isprs-annals-X-4-W1-2022-537-2023},
    archivePrefix={arXiv},
    eprint={2406.15118},
    primaryClass={cs.CV}
}