SIM2E: Benchmarking the Group Equivariant Capability of Correspondence Matching Algorithms
This provides a benchmark for researchers in computer vision and robotics to assess and improve equivariant matching methods, though it is incremental as it focuses on evaluation rather than new algorithms.
The paper tackles the problem of evaluating sim(2)-equivariant correspondence matching algorithms by introducing a specialized dataset and benchmarking 16 state-of-the-art approaches, showing the importance of group equivariant algorithms under various transformation conditions.
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.