Perspective Transformation Layer

TL;DR

This paper introduces a perspective transformation layer for deep learning that learns homography matrices, enabling multi-view analysis and better reflection of geometric position changes between observers and objects.

Contribution

The paper proposes a novel perspective transformation layer that learns homography matrices directly, allowing multi-view analysis without extra module parameters.

Findings

The layer effectively learns homography transformations.

It improves multi-view analysis in deep learning models.

Experimental results confirm its superiority.

Abstract

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.