AbstractWe explain that the task of multiple rigid motion segmentation and estimation from image feature point correspondence demands an estimator of high robustness. We show that a heuristics‐based partial modeling approach can be used to develop a highly robust estimator called the MF estimator for general regression, where “MF” represents an abbreviation of Model Fitting. Finally, we provide experimental results in estimating single rigid motion from a mixture of 2D‐2D (or image‐image), 3D‐2D (or range‐image) and 3D‐3D (or range‐range) corresponding point data by using the proposed MF estimator. As will be seen, only four well matched corresponding point pairs are needed to get a good estimate of motion parameters no matter how many mismatched corresponding point pairs (or outliers) occur. The article represents an initial effort towards robust imag
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