  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.
