Learning Local Features by Reinforcing Spatial Structure Information

Abstract

Learning-based local feature extraction algorithms have advanced considerably in terms of robustness. While excelling at enhancing feature robustness, some outstanding algorithms tend to neglect discriminability—a crucial aspect in vision tasks. With the increase of deep learning convolutional layers, we observe an amplification of semantic information within images, accompanied by a diminishing presence of spatial structural information. This imbalance primarily contributes to the subpar feature discriminability. Therefore, this paper introduces a novel network framework aimed at imbuing feature descriptors with robustness and discriminative power by reinforcing spatial structural information. Our approach incorporates a spatial structure enhancement module into the network architecture, spanning from shallow to deep layers, ensuring the retention of rich structural information in deeper layers, thereby enhancing discriminability. Finally, we evaluate our method, demonstrating superior performance in visual localization and feature-matching tasks.