R-SAC: Reinforcement Sample Consensus
The rejection of outliers in observed data is the foundation for accurate model estimation. Random sample consensus (RANSAC) is a classical algorithm aiming to find the inliers for robust model estimation. After sampling a series of minimal sets that can support the hypothesis estimation and generating the respective hypotheses, the best hypothesis that earns the maximum consensus is chosen for the final model estimation. However, this strategy may face exponentially computational growth as the outlier ratio increases. Besides, fitting a model from a minimal set may hinder the accurate model estimation especially when the inliers are extremely rare. In contrast, a model estimated from more observations may be better than from a minimum set. To approach such problem, we propose reinforcement sample consensus (R-SAC) to train a neural network to classify the inliers and outliers among all the correspondences with reinforcement learning. During training, we regard the number of inliers as a reward and encourage the agent to find the optimal subset supporting the final model estimation in a unsupervised manner. During inference, the R-SAC network is able to directly generate the inlier set, which could significantly reduce the computational resources in sampling and is able to select a more robust model hypothesis fitted from more correspondences. Empirical results show that our method achieves comparable performance compared with the previous supervised counterparts and remarkable efficiency especially when the outlier ratio is large.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
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 robus…
Graph Neural Networkreinforcement-learningReinforcement LearningHi-TTRL: Regulating Consensus with Hints for Test-Time Reinforcement Learning
Test-time reinforcement learning (TTRL) improves the reasoning capabilities of large language models without labeled data by updating the policy with pseudo-labels constructed through majority voting. While effective, th…
Reinforcement LearningReaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination
Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However, current cooperative multi-agent reinfo…
Multi-agent Reinforcement LearningTrust-based Consensus in Multi-Agent Reinforcement Learning Systems
An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During centralized training, agents can be guided by the same sign…
Contrastive LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1