Unsupervised Learning for Maximum Consensus Robust Fitting: A Reinforcement Learning Approach
Date
2023
Authors
Truong, G.
Le, H.
Zhang, E.
Suter, D.
Gilani, S.Z.
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Journal article
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023; 45(3):3890-3903
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Giang Truong, Huu Le, Erchuan Zhang, David Suter, and Syed Zulqarnain Gilani
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Abstract
Robust model fitting is a core algorithm in several computer vision applications. Despite being studied for decades, solving this problem efficiently for datasets that are heavily contaminated by outliers is still challenging: due to the underlying computational complexity. A recent focus has been on learning-based algorithms. However, most of these approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework: that learns to directly (without labelled data) solve robust model fitting. Moreover, unlike other learning-based methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing (un)supervised learning approaches, and also achieves competitive results compared to traditional (non-learning-based) methods. Our approach is designed to try to maximise consensus (MaxCon), similar to the popular RANSAC. The basis of our approach, is to adopt a Reinforcement Learning framework. This requires designing appropriate reward functions, and state encodings. We provide a family of reward functions, tunable by choice of a parameter. We also investigate the application of different basic and enhanced Q-learning components.
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