The correspondence focuses on the robust 3-D-3-D pose estimation, especially, multiple pose estimation. The robust 3-D-3-D multiple pose estimation problem is formulated as a series of general regressions which involve a successively size-decreasing data set, with each regression relating to one particular pose of interest. Since the first few regressions may carry a severely contaminated Gaussian error noise model, the MF-estimator (Zhuang et al., 1992) is used to solve each regression for each pose of interest. Extensive computer experiments with both real imagery and simulated data are conducted and results are promising. Three distinctive features of the MF-estimator are theoretically discussed and experimentally demonstrated: It is highly robust in the sense that it is not much affected by a possible large portion of outliers or incorrect matches as long as the minimum number of inliers necessary to give a unique solution are provided; It is made virtually independent of initial guesses; It is computationally reasonable and admits an efficient parallel implementation.