be randomly sampled, and the probability of the algorithm succeeding depends on the proportion of inliers in the data as well as the choice of several algorithm parameters. Overview The RANSAC algorithm is a learning technique to estimate parameters of a model by random sampling of observed data. Given a dataset whose data elements contain both inliers and outliers, RANSAC uses the voting scheme to find the optimal fitting result. Data elements in the dataset are used to vote for one or multiple models. The implementation of this voting scheme is based on two assumptions: that the noisy features will not vote consistently for any single model (few outliers) and there are enough features to agree on a good model (few missing data). The RANSAC algorithm is essentially composed of two steps that are iteratively repeated: In the first step, a sample subset containing minimal data items is randomly selected from the input dataset. A fitting model with model parameters is computed using only the elements of this sample subset. The cardinality of the sample subset (e.g., the amount of data in this subset) is sufficient to determine the model parameters. In the second step, the algorithm checks which elements of the entire dataset are consistent with the model instantiated by the estimated model parameters obtained from the first step. A data element will be considered as an outlier if it does not fit the model within some error threshold defining the maximum data deviation of inliers. (Data elements beyond this deviation are outliers.) The set of inliers obtained for the fitting model is called the consensus set. The RANSAC algorithm will iteratively repeat the above two steps until the obtained consensus set in certain iteration has enough inliers. The input to the RANSAC algorithm is a set of observed data values, a model to fit to the observations, and some confidence parameters defining outliers. In more details than the aforementioned RANSAC algorithm overview, RANSAC achieves its goal by repeating the following steps: Select a random subset of the original data. Call this subset the hypothetical inliers. A model is fitted to the set of hypothetical inliers. All data are then tested against the fitted model. All the data points (of the original data) that fit the estimated model well, according to some model-specific loss function, are called the consensus set (i.e., the set of inliers for the model). The