RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

TL;DR

RLSAC introduces a reinforcement learning-based framework utilizing graph neural networks for end-to-end robust estimation, effectively leveraging data features and memory to improve sampling in noisy environments.

Contribution

It is the first to apply reinforcement learning to sampling consensus for end-to-end robust estimation, integrating data and memory features for improved sampling guidance.

Findings

RLSAC outperforms traditional methods in robustness and accuracy.

The method demonstrates strong transferability to other robust estimation tasks.

Experimental results validate the effectiveness of feature-guided sampling.

Abstract

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://github.com/IRMVLab/RLSAC.