Acquiring qualified samples for RANSAC using geometrical constraints

Estimating parameters of a geometrical model from 3-D point cloud data is an important problem in computer vision. Random sample consensus (RANSAC) and its variations have been proposed for the estimation of the models parameters. However, RANSAC is computationally expensive and the problem is challenging when the measured 3-D data contain noise and outliers. This paper presents an efficient sampling technique for RANSAC, in which geometrical constraints are utilized for selecting good samples for a robust estimation. The constraints are based on two predefined criteria. First, the samples must ensure being consistent with the estimated model; second, the selected samples must satisfy explicit geometrical constraints of the interested objects. The proposed approach is wrapped as a robust estimator, named GCSAC (Geometrical Constraint SAmple Consensus), for estimating a cylindrical object from a 3-D point cloud. Extensive experiments on various data sets show that our method outperforms other robust estimators (e.g. MLESAC) tested in term of both precision of the estimated model and computational time. The implementations and evaluation datasets used in this paper are made publicly available.