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Robust surface perception in range data

We present an autonomous, statistically robust, sequential function approximation approach to simultaneous parameterization and organization of (possibly partially occluded) surfaces in noisy, outlier-ridden (not Gaussian) range data. Unlike existing surface characterization techniques, our method generates complete surface hypotheses in parameter space. Given a noisy depth map of an unknown 3D scene, the algorithm first selects appropriate seed points representing possible surfaces. For each non-redundant seed it chooses the best approximating model from a given set of competing models using the Modified Akaike Information Criterion (MCAIC). With this best model, each surface is expanded from its seed over the entire image, and this step is repeated for all seeds. Those points which appear to be outliers with respect to the model in growth are not included in the (possibly disconnected) surface. Point regions are removed from each newly grown surface in the prune stage. Noise, outliers, or coincidental surface alignment may cause some points to appear to belong to more than one surface. These ambiguities are resolved by a weighted voting scheme within a 5 X 5 decision window centered around the ambiguous point. The isolated point regions left after the resolve stage are removed and any missing points in the data are filled by the surface having a majority consensus in an 8-neighborhood.