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

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

Terahertz Polarimetric Imaging

Imaging in the terahertz (THz) regime (0.1-10 THz) has shown grown interest over the past couple decades. While the THz imaging field is maturing, little work has been done imaging at these frequencies using polarization as a contrast mechanism. The polarization state of light as it interacts with different media contains additional information such as surface features, birefringence, and diattentuation. In this thesis, a polarization sensitive THz single-pixel imaging system is demonstrated using a recently developed THz quantum-cascade external cavity surface emitting laser (QC-VECSEL). The QC-VECSEL provides a high power THz laser output with good beam quality, features that are ideal for single-pixel imaging. The polarization sensitive images are acquired by using a fast rotating polarizer as an analyzer. This modulates the polarization state of the image, allowing for lock-in detection. Additionally, comparisons between past imaging systems are made to spotlight specific improvements achieved in this work

Depth Sensing Using Geometrically Constrained Polarization Normals

Analyzing the polarimetric properties of reflected light is a potential source of shape information. However, it is well-known that polarimetric information contains fundamental shape ambiguities, leading to an underconstrained problem of recovering 3D geometry. To address this problem, we use additional geometric information, from coarse depth maps, to constrain the shape information from polarization cues. Our main contribution is a framework that combines surface normals from polarization (hereafter polarization normals) with an aligned depth map. The additional geometric constraints are used to mitigate physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We believe our work may have practical implications for optical engineering, demonstrating a new option for state-of-the-art 3D reconstruction.MIT Media Lab Consortiu