The aim of this paper is to demonstrate that a state of the art feature
matcher (LoFTR) can be made more robust to rotations by simply replacing the
backbone CNN with a steerable CNN which is equivariant to translations and
image rotations. It is experimentally shown that this boost is obtained without
reducing performance on ordinary illumination and viewpoint matching sequences.

本文旨在证明用可旋转卷积神经网络替换骨干卷积神经网络能够使现有的特征匹配算法（LoFTR）在旋转处理方面更加鲁棒，实验证明这样的改进不会减少对普通对比度和角度匹配序列的处理性能。

在最先进的特征匹配器中使用旋转不变特征的案例

A case for using rotation invariant features in state of the art feature  matchers

Steerable CNN imposes the prior knowledge of transformation invariance or
equivariance in the network architecture to enhance the the network robustness
on geometry transformation of data and reduce overfitting. It has been an
intuitive and widely used technique to construct a steerable filter by
augmenting a filter with its transformed copies in the past decades, which is
named as filter transform in this paper. Recently, the problem of steerable CNN
has been studied from aspect of group representation theory, which reveals the
function space structure of a steerable kernel function. However, it is not yet
clear on how this theory is related to the filter transform technique. In this
paper, we show that kernel constructed by filter transform can also be
interpreted in the group representation theory. This interpretation help
complete the puzzle of steerable CNN theory and provides a novel and simple
approach to implement steerable convolution operators. Experiments are executed
on multiple datasets to verify the feasibility of the proposed approach.

本文介绍一种名为 “可转向 CNN” 的神经网络结构，应用于数据的几何变换，通过滤波器变换技术构建可转向卷积核，通过群表示理论将此两者联系在一起，提高了网络的鲁棒性并减少了过拟合。