Extracting discriminative local features that are invariant to imaging
variations is an integral part of establishing correspondences between images.
In this work, we introduce a self-supervised learning framework to extract
discriminative rotation-invariant descriptors using group-equivariant CNNs.
Thanks to employing group-equivariant CNNs, our method effectively learns to
obtain rotation-equivariant features and their orientations explicitly, without
having to perform sophisticated data augmentations. The resultant features and
their orientations are further processed by group aligning, a novel invariant
mapping technique that shifts the group-equivariant features by their
orientations along the group dimension. Our group aligning technique achieves
rotation-invariance without any collapse of the group dimension and thus
eschews loss of discriminability. The proposed method is trained end-to-end in
a self-supervised manner, where we use an orientation alignment loss for the
orientation estimation and a contrastive descriptor loss for robust local
descriptors to geometric/photometric variations. Our method demonstrates
state-of-the-art matching accuracy among existing rotation-invariant
descriptors under varying rotation and also shows competitive results when
transferred to the task of keypoint matching and camera pose estimation.

本实验引入自监督学习框架，使用群等变 CNN 提取旋转不变的局部特征，通过群对齐技术实现旋转不变性，并通过对比描述符损失和方向对齐损失训练来得到具有鲁棒性的局部特征描述符，表现出在不同环境下的高精度匹配结果，并在关键点匹配和相机姿态估计任务中显示出有竞争力的结果。