Correspondence Learning via Group-wise Motion Enhancement

The outlier removal methods are usually based on Multi-Layer Perceptron (MLP) for capturing context, which neglect the underlying motion information in images. Recently, CNN-based methods have attempted to address this issue by estimating motion fields and have made significant progress. However, CNNs tend to diminish the discontinuities of motion fields, making it difficult for the network to capture multiple motion patterns in complex scenes. Therefore, we propose a Group-wise Motion Enhancement (GME) block that groups motion fields along the channel dimension to learn multiple motion patterns. This enables the network to identify inliers under different motion patterns more accurately. Experiments show that compared with several existing methods, our method achieves advanced performance on the relative pose estimation task.