GCSAC: geometrical constraint sample consensus for primitive shapes estimation in 3D point cloud

Estimating parameters of a primitive shape from a 3-D point cloud data is a challenging problem due to data containing noises and computational time demand. In this paper, we present a new robust estimator (named GCSAC, Geometrical Constraint SAmple Consensus) aimed at solving such issues. The proposed algorithm takes into account geometrical constraints to construct qualified samples for the estimation. Instead of randomly drawing minimal subset of sample, explicit geometrical properties of the interested primitive shapes (e.g., cylinder, sphere and cone) are used to drive sampling procedures. At each iteration of GCSAC, the minimal subset sample is selected based on two criteria (1) It must ensure a consistency with the estimated model via a roughly inlier ratio evaluation; (2) The samples satisfy geometrical constraints of the interested objects. Based on the obtained good samples, model estimation and verification procedures of the robust estimator are deployed in GCSAC. Extensive experiments have been conducted on synthesized and real datasets for evaluation. Comparing with the common robust estimators of RANSAC family (RANSAC, PROSAC, MLESAC, MSAC, LO-RANSAC and NAPSAC), GCSAC outperforms in term of both the precision of the estimated model and computational time. The implementations of the proposed method and the datasets are made publicly available.