Rotation-Equivariant Robot Vision: A Perspective via Correspondence-Matching and Pre-training

Abstract

Correspondence matching is a fundamental and crucial task in robot vision. In recent years, deep learning-based keypoint matching techniques have shown outstanding performance in downstream tasks. Conventional learning-based correspondence matching methods rely on large datasets and a specific training procedure. Correspondence techniques based on pre-trained features have been preliminarily explored by researchers. Unfortunately, traditional convolutional neural networks only possess translation invariance but lack rotational invariance, hence, their performance suffers significantly under heavy rotations. Therefore, we propose a correspondence matching method based on pre-trained group-equivariant neural networks and compare the performance of various rotation-equivariant to rotation-invariant transformers. We conducted experiments on the Rotated-Hpatches and Rotated-MegaDepth datasets, and the results indicate that our proposed method is concise and effective, achieving state-of-the-art performance without the need for retraining in downstream tasks.