EB-RANSAC: Random Sample Consensus based on Energy-Based Model
This is an incremental improvement for researchers and practitioners in computer vision and statistics, offering a simpler alternative to RANSAC.
The paper tackles the problem of robust estimation by proposing EB-RANSAC, an energy-based model that eliminates the need for repetitive sampling and reduces hyperparameters, demonstrating effectiveness in linear regression and maximum likelihood estimation with numerical results.
Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In this study, an energy-based model (EBM) for robust estimation that has a similar scheme to RANSAC, energy-based RANSAC (EB-RANSAC), is proposed. EB-RANSAC is applicable to a wide range of estimation problems similar to RANSAC. However, unlike RANSAC, EB-RANSAC does not require a troublesome sampling procedure and has only one hyperparameter. The effectiveness of EB-RANSAC is numerically demonstrated in two applications: a linear regression and maximum likelihood estimation.