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 each convolution kernel and improves 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 ensembles features extracted from multiple rotated versions of input images and can provide auxiliary information for the training of RKF by leveraging the knowledge 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 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：在存在较大旋转变化的情况下，局部特征描述符的性能会下降。为解决此问题，我们提出了一种学习旋转不变描述符的高效方法。具体而言，我们提出了旋转核融合（RKF），该方法对每个卷积核施加旋转，从而增强CNN的固有特性。由于RKF可通过后续重参数化进行处理，因此在推理阶段不会引入额外计算开销。此外，我们提出了多方向特征聚合（MOFA），该方法集成了来自输入图像多个旋转版本的特征，并可通过利用知识蒸馏策略为RKF的训练提供辅助信息。我们将蒸馏后的RKF模型称为DRKF。除了在公开数据集HPatches的旋转增强版本上评估外，我们还贡献了一个名为DiverseBEV的新数据集，该数据集包含具有大视点变化和相机旋转的鸟瞰图。大量实验表明，在面临较大旋转变化时，我们的方法能够优于其他现有技术。