EV-CGNet: Co-visible Focused 3D-guided 2D Event Keypoint Detection Network

Event keypoint detection has garnered significant attention due to its crucial role in extracting spatial relationships between matched keypoints, which are fundamental for various computer vision tasks. However, achieving robust event keypoint detection remains challenging the difficulty in balancing the exploitation of event information and compatibility with established algorithms. Moreover, the limited use of co-visible information often results in excessive keypoint detection in non-matching regions, leading to incorrect matches. To address these challenges, we propose a novel Co-visible Focused 3D-guided 2D Event Keypoint Detection Network (EV-CGNet), which mainly consists of a 3D-guided 2D feature prototype learning (G2PL) module and a co-visible region-focused detector and descriptor learning (CDDL) module. The proposed method enjoys several merits. First, the proposed G2PL module can enhance event frame feature prototypes by recovering motion information with guidance from event points. Second, the proposed CDDL module can direct keypoint detection toward co-visible regions and ensure accurate matches. Comprehensive experimental evaluations on six challenging benchmarks show that our method surpasses state-of-the-art event keypoint detection method significantly.