Prediction intervals for surface growing range segmentation

The surface growing framework presented by P. Besl and R. Jain (1988) has served as the basis for many range segmentation techniques. It has been augmented with alternative fitting techniques, model selection criteria, and solid modelling components. All of these approaches, however require global thresholds and large isolated seed regions. Range scenes typically do not satisfy the global threshold assumption since it requires data noise characteristics to be constant throughout the scene. Furthermore, as scene complexity increases, the number of surfaces, discontinuities, and outliers increase, hindering the identification of large seed regions. We present statistical criteria based on multivariate regression to replace the traditional decision criteria used in surface growing. We use local estimates and their uncertainties to construct criteria which capture the uncertainty in extrapolating estimated fits. We restrict surface expansion to very localized extrapolations, increasing the sensitivity to discontinuities and allowing regions to refine their estimates and uncertainties. Our approach uses a small number of parameters which are either statistical thresholds or cardinality measures, i.e. we do not use thresholds defined by specific range distances or orientation angles