Unsupervised learning for maximum consensus robust fitting: A reinforcement learning approach

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.

RAS ID

44438

Document Type

Journal Article

Date of Publication

3-2023

Funding Information

Australian Research Council

School

School of Science

Grant Number

ARC Number : DP200103448

Copyright

subscription content

Publisher

IEEE

Comments

G. Truong, H. Le, E. Zhang, D. Suter and S. Z. Gilani, (2023). Unsupervised learning for maximum consensus robust fitting: A reinforcement learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3890-3903.

https://doi.org/10.1109/TPAMI.2022.3178442

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Link to publisher version (DOI)

10.1109/TPAMI.2022.3178442