ConDL: Detector-Free Dense Image Matching

[Submitted on 5 Aug 2024]

View a PDF of the paper titled ConDL: Detector-Free Dense Image Matching, by Monika Kwiatkowski and 2 other authors

Abstract:In this work, we introduce a deep-learning framework designed for estimating dense image correspondences. Our fully convolutional model generates dense feature maps for images, where each pixel is associated with a descriptor that can be matched across multiple images. Unlike previous methods, our model is trained on synthetic data that includes significant distortions, such as perspective changes, illumination variations, shadows, and specular highlights. Utilizing contrastive learning, our feature maps achieve greater invariance to these distortions, enabling robust matching. Notably, our method eliminates the need for a keypoint detector, setting it apart from many existing image-matching techniques.