A Reliable Interest Point Detection

Interest point detection is a fundamental task in computer vision, with applications in image matching, 3D reconstruction, and image stitching. Traditional methods often produce numerous weak and unstable keypoints that are sensitive to noise, viewpoint changes, and illumination variations, adversely affecting subsequent tasks like matching and reconstruction. To mitigate this, a deep learning architecture incorporating dual-network detection consistency is proposed. This approach integrates two independent detection networks and employs a consensus mechanism, retaining only the keypoints identified by both networks to filter out unreliable candidates. Experimental results demonstrate that this framework achieves an average matching accuracy of 90\% on the HPatches dataset, surpassing the overall performance of current mainstream algorithms.