The performance of local feature descriptors degrades in the presence of large rotation variations. To address this issue, we present an efficient approach to learning rotation invariant descriptors. Specifically, we propose Rotated Kernel Fusion (RKF) which imposes rotations on the convolution kernel to improve the inherent nature of CNN. Since RKF can be processed by the subsequent re-parameterization, no extra computational costs will be introduced in the inference stage. Moreover, we present Multi-oriented Feature Aggregation (MOFA) which aggregates features extracted from multiple rotated versions of the input image and can provide auxiliary knowledge for the training of RKF by leveraging the distillation strategy. We refer to the distilled RKF model as DRKF. Besides the evaluation on a rotation-augmented version of the public dataset HPatches, we also contribute a new dataset named DiverseBEV which is collected during the drone's flight and consists of bird's eye view images with large viewpoint changes and camera rotations. Extensive experiments show that our method can outperform other state-of-the-art techniques when exposed to large rotation variations.

翻译：局部特征描述符在大旋转变化下性能会显著下降。为解决此问题，我们提出一种学习旋转不变描述符的高效方法。具体而言，我们设计了旋转核融合（RKF），通过对卷积核施加旋转变换以改进CNN的固有特性。由于RKF可通过后续重参数化处理，推理阶段不会引入额外计算开销。此外，我们提出多方向特征聚合（MOFA），该方法聚合输入图像多个旋转版本中提取的特征，并利用蒸馏策略为RKF训练提供辅助知识。我们将蒸馏后的RKF模型称为DRKF。除在旋转增强版公开数据集HPatches上进行评估外，我们还贡献了一个名为DiverseBEV的新数据集，该数据集在无人机飞行过程中采集，包含大视角变化和相机旋转的鸟瞰图。大量实验表明，在面对大旋转变化时，我们的方法能够超越其他现有最优技术。