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Many tasks, such as pose determination, object recognition and model building rely on a geometrical description of the visible surfaces derived from scene measurements. Even though much effort has been invested in surface description, robust extraction of surface parameters from scattered and noisy 3-D measurements is still a delicate task. For a given visible surface section, the extracted parameters should not depend on the sensor position in the scene nor on a particular measurement set on this surface. In this work, we show that a viewpoint invariant and stable local surface description can be extracted on sections where measurement constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify such stable sections. The approach is based on a measurement error model which takes into account the sensor’s viewpoint. An application of the approach to the extraction of straight line sections from single scan 3-D surface profiles is presented. The extracted stable linear sections are stored in a list that includes the estimated descriptive parameters for each section and indices of reliability for each description. The descriptive parameters obtained from images of the objects is compared with pre-measured parameters.

Many tasks, such as pose determination, object recognition and model building rely on a geometrical description of the visible surfaces derived from scene measurements. Even though much effort has been invested in surface description, robust extraction of surface parameters from scattered and noisy 3-D measurements is still a delicate task. For a given visible surface section, the extracted parameters should not depend on the sensor position in the scene nor on a particular measurement set on this surface. In this work, we show that a viewpoint invariant and stable local surface description can be extracted on sections where measurement constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify such stable sections. The approach is based on a measurement error model which takes into account the sensor's viewpoint. An application of the approach to the extraction of straight line sections from single scan 3-D surface profiles is presented. The extracted stable linear sections are stored in a list that includes the estimated descriptive parameters for each section and indices of reliability for each description. The descriptive parameters obtained from images of the objects is compared with pre-measured parameters.