Correspondence matching is a fundamental problem in computer vision and
robotics applications. Solving correspondence matching problems using neural
networks has been on the rise recently. Rotation-equivariance and
scale-equivariance are both critical in correspondence matching applications.
Classical correspondence matching approaches are designed to withstand scaling
and rotation transformations. However, the features extracted using
convolutional neural networks (CNNs) are only translation-equivariant to a
certain extent. Recently, researchers have strived to improve the
rotation-equivariance of CNNs based on group theories. Sim(2) is the group of
similarity transformations in the 2D plane. This paper presents a specialized
dataset dedicated to evaluating sim(2)-equivariant correspondence matching
algorithms. We compare the performance of 16 state-of-the-art (SoTA)
correspondence matching approaches. The experimental results demonstrate the
importance of group equivariant algorithms for correspondence matching on
various sim(2) transformation conditions. Since the subpixel accuracy achieved
by CNN-based correspondence matching approaches is unsatisfactory, this
specific area requires more attention in future works. Our dataset is publicly
available at: mias.group/SIM2E.

论文提出了基于 group theories 针对 sim (2)-equivariant correspondence matching 的专门数据集，并对 16 种最新的相关算法进行了实验比对，结果表明了算法的对应模糊度和精度，同时也提高了对该领域的重视程度。