Papers › PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
Jérome Revaud, Vincent Leroy, Philippe Weinzaepfel, Boris Chidlovskii
Existing approaches for learning local image descriptors have shown remarkable achievements in a wide range of geometric tasks. However, most of them require per-pixel correspondence-level supervision, which is difficult to acquire at scale and in high quality. In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision. Given two images depicting the same scene, we extract pixel descriptors and build a correlation volume. The first prior enforces the local consistency of matches in this volume via a pyramidal structure iteratively constructed using a non-parametric module. The second prior exploits the fact that each descriptor should match with at most one descriptor from the other image. We combine our unsupervised loss with a standard self-supervised loss trained from synthetic image augmentations. Feature descriptors learned by the proposed approach outperform their fully- and self-supervised counterparts on various geometric benchmarks such as visual localization and image matching, achieving state-of-the-art performance. Project webpage: https://europe.naverlabs.com/research/3d-vision/pump
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