Improving the matching of deformable objects by learning to detect keypoints
This addresses the challenge of matching deformable objects in computer vision, with incremental improvements in keypoint detection for specific applications like object retrieval.
The paper tackles the problem of non-rigid image correspondence by proposing a learned keypoint detection method that increases correct matches, resulting in a 20 percentage point improvement over state-of-the-art detectors on real images of non-rigid objects.
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor extractor, we train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. For that, we apply geometric and photometric warpings to images to generate a supervisory signal, allowing the optimization of the detector. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method, while outperforming the state-of-the-art keypoint detectors on real images of non-rigid objects by 20 p.p. We also apply our method on the complex real-world task of object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task. The source code and trained models are publicly available at https://github.com/verlab/LearningToDetect_PRL_2023