iMatching: Imperative Correspondence Learning

• URL: http://arxiv.org/abs/2312.02141v2
• Date: Wed, 31 Jul 2024 17:41:14 GMT
• Title: iMatching: Imperative Correspondence Learning
• Authors: Zitong Zhan, Dasong Gao, Yun-Jou Lin, Youjie Xia, Chen Wang,
• Abstract summary: We introduce a new self-supervised scheme, imperative learning (IL), for training feature correspondence. It enables correspondence learning on arbitrary uninterrupted videos without any camera pose or depth labels. We demonstrate superior performance on tasks including feature matching and pose estimation.
• Score: 5.568520539073218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning feature correspondence is a foundational task in computer vision, holding immense importance for downstream applications such as visual odometry and 3D reconstruction. Despite recent progress in data-driven models, feature correspondence learning is still limited by the lack of accurate per-pixel correspondence labels. To overcome this difficulty, we introduce a new self-supervised scheme, imperative learning (IL), for training feature correspondence. It enables correspondence learning on arbitrary uninterrupted videos without any camera pose or depth labels, heralding a new era for self-supervised correspondence learning. Specifically, we formulated the problem of correspondence learning as a bilevel optimization, which takes the reprojection error from bundle adjustment as a supervisory signal for the model. To avoid large memory and computation overhead, we leverage the stationary point to effectively back-propagate the implicit gradients through bundle adjustment. Through extensive experiments, we demonstrate superior performance on tasks including feature matching and pose estimation, in which we obtained an average of 30% accuracy gain over the state-of-the-art matching models. This preprint corresponds to the Accepted Manuscript in European Conference on Computer Vision (ECCV) 2024.

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