  Incorporating geometric transformations that reflect the relative position
changes between an observer and an object into computer vision and deep
learning models has attracted much attention in recent years. However, the
existing proposals mainly focus on the affine transformation that is
insufficient to reflect such geometric position changes. Furthermore, current
solutions often apply a neural network module to learn a single transformation
matrix, which not only ignores the importance of multi-view analysis but also
includes extra training parameters from the module apart from the
transformation matrix parameters that increase the model complexity. In this
paper, a perspective transformation layer is proposed in the context of deep
learning. The proposed layer can learn homography, therefore reflecting the
geometric positions between observers and objects. In addition, by directly
training its transformation matrices, a single proposed layer can learn an
adjustable number of multiple viewpoints without considering module parameters.
The experiments and evaluations confirm the superiority of the proposed layer.
