# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 2 Related Works
Another method is to determine a Gaussian distribution fitting to the smallest residuals in the dataset (modified selective statistical estimator (MSSE) by [20]). While this method is more robust against outliers, it is sensitive to the distinction of inliers from outliers. One strategy to eliminate the requirement of a fixed threshold is to run RANSAC several times using a range of pre-determined thresholds (the stable random sample consensus (StaRSAC) method by [21]). However, depending on the range of the thresholds to be tested and the number of RANSAC executions, this strategy can be computationally exhaustive. In the RANSAC with uncertainty estimation (Cov-RANSAC) algorithm, the uncertainty of the model estimated from the minimal sample set is used to determine a subset of potential inliers, to which the ordinary RANSAC is applied afterwards [22]. However, the uncertainty of the estimated model highly depends on the uncertainty of the configuration of the points appearing in the minimal sample set. The sampling strategy in RANSAC is also an important factor, as it influences the algorithm efficiency with respect to both the number of RANSAC iterations and degeneracy (Degeneracy in robust epipolar-geometry estimation occurs when one or more degenerate configurations exist in the scene. This usually happens when the majority of the correspondences belong to a dominant plane in the scene and the rest of the correspondences are not on the plane (planar degeneracy), or when the correspondences belong to a very small region of an image (ill-configuration).) of the estimated model. There are several methods to control each of these two factors. The main contributions in this regard are discussed here. To control the speed of the algorithm, two strategies can be applied. The first strategy is to enforce an initial consistency check to filter the putative correspondences. This consistency can be measured as the fraction of neighbouring features in a region around a point in one image whose correspondences fall into the similar region in the other image (Spatially Consistent Random Sample Consensus (SCRAMSAC) method by [23]). This strategy is sensitive to the region size and the threshold used to define the spatial consistency. Alternatively, sampling can be guided by keypoint matching scores (Progressive Sample Consensus (PROSAC) method [24], Efficient Guided Hypothesis Generation (EGHG) method by [25]). However, such a strategy is not effective when foreground motion happens. In addition, the scenes with repetitive textures may result in many false matches with high matching scores. Another example of such strategies is to assume that correspondences have a natural grouping structure, in which some of the groups have higher inlier probability than others (the GroupSAC method by [26]). However, finding a meaningful and efficient grouping among the correspondences is itself a concerning challenge in different applications. The strategies mentioned so far mostly require supplementary information about the scene or the matches. Comparatively, guided sampling based on the information from sorted residuals can be used to accelerate the hypothesis generation while avoiding any application-specific ordering or grouping technique (Multi-Structure Hypothesis Generation (Multi-GS) by [27]). In this method, sampling is guided towards selecting the points that are rising from the same structure. This strategy speeds up the procedure to reach an outlier-free sample set. However, this method causes more computational complexity, since every point in the hypothesized sample set should be compared against all of the other points in the dataset in order to determine its intersection (in terms of structure) with them. The fast consensus sampling (FCS) method based on the residuals is developed by [28]. In this method, proposal probabilities are calculated for the correspondences based on their normalized residuals and a concentration score.