High Resolution Surface Reconstruction of Cultural Heritage Objects Using Shape from Polarization Method
<|reference_start|>High Resolution Surface Reconstruction of Cultural Heritage Objects Using Shape from Polarization Method: Nowadays, three-dimensional reconstruction is used in various fields like computer vision, computer graphics, mixed reality and digital twin. The three-dimensional reconstruction of cultural heritage objects is one of the most important applications in this area which is usually accomplished by close range photogrammetry. The problem here is that the images are often noisy, and the dense image matching method has significant limitations to reconstruct the geometric details of cultural heritage objects in practice. Therefore, displaying high-level details in three-dimensional models, especially for cultural heritage objects, is a severe challenge in this field. In this paper, the shape from polarization method has been investigated, a passive method with no drawbacks of active methods. In this method, the resolution of the depth maps can be dramatically increased using the information obtained from the polarization light by rotating a linear polarizing filter in front of a digital camera. Through these polarized images, the surface details of the object can be reconstructed locally with high accuracy. The fusion of polarization and photogrammetric methods is an appropriate solution for achieving high resolution three-dimensional reconstruction. The surface reconstruction assessments have been performed visually and quantitatively. The evaluations showed that the proposed method could significantly reconstruct the surfaces' details in the three-dimensional model compared to the photogrammetric method with 10 times higher depth resolution.<|reference_end|>
@article{mortazavi2024high,
    title={High Resolution Surface Reconstruction of Cultural Heritage Objects
  Using Shape from Polarization Method},
    author={F. S. Mortazavi and M. Saadatseresht},
    journal={XLVIII-2/W2-2022},
    year={2024},
    doi={10.5194/isprs-archives-XLVIII-2-W2-2022-85-2022},
    archivePrefix={arXiv},
    eprint={2406.15121},
    primaryClass={cs.CV eess.IV}
}