Local Feature Extraction Method Based on Dual Attention Network for Forest Scenes

The existing learning-based image feature point extraction methods ignore the contextual information in the image, and perform poorly in repetitive and weak texture scenes such as forest scenes, which brings great difficulties to the 3D reconstruction task of forest scenes. In order to solve the above problems, this paper proposes a new self-supervised image feature point extraction method with attention mechanism. First, the input image is processed by an encoder and encoded into a feature map containing multi-scale information. Then, the feature map is fed into the dual attention mechanism module, which includes a position attention module and a channel attention module, so as to obtain the image feature correlation in the spatial dimension and the channel dimension respectively, and obtain the enhanced feature map. Finally, the enhanced feature map is used to train the detector and descriptor respectively. Experiments show that our method has achieved some improvement on both the public dataset Hpatches and the virtual dataset Tartan Air, especially in the weakly textured forest scene.