DeepMatch: Navigating the Complexities of Underwater Textures for Enhanced Keypoint Matching

Abstract

Driven by demands for oceanic exploration, advancements in 3D visual tasks based on video frames are essential. Keypoint matching, essential for camera pose and motion, is hindered by the unique challenges of underwater imagery, such as sparse and repetitive textures. To tackle these issues, we introduce the DeepMatch framework, tailored for aquatic environments. It comprises two main components: a feature extraction network with a multi-level feature fusion module for enhanced keypoint detection in sparse textures, and an attention-based graph neural network with relative position encoding to correct matches in repetitive texture areas. Our experiments demonstrate that DeepMatch surpasses traditional and learning-based methods in underwater image matching, providing a robust solution for accurate camera pose estimation.