An abstraction-based approach to 3-D pose determination from range images
Academic Article
Overview
Identity
Additional Document Info
Other
View All
Overview
abstract
An abstraction-based paradigm that makes explicit the process of imposing assumptions on data has been developed. The units of abstraction are models whose levels of abstraction are determined by the degree of assumption necessary for their application. A general-to-specific refinement process provides a mechanism to proceed gracefully through the abstraction hierarchy. The task of object recognition and pose determination becomes one of making increasingly stronger assumptions about the data. These assumptions yield model hypotheses that may then be applied and tested. This process of assumption and abstraction furnishes a path between symbolic descriptors of objects in the scene to their numeric specification. Throughout the process, abstractions are made to interpret better the original data to which the hypothesized models are fitted/computed. The strategy is thus data bound. This strategy was applied to the recognition and pose determination of objects comprising simple and compound cylindrical and planar surfaces in dense range data. For compound surfaces, especially, the assumptions of scene contents are necessary before specific split-and-merge operators can be used to segment the image. A method of computing reliable Gaussian and mean curvature sign-map descriptors from the polynomial approximations of surfaces is demonstrated. Such descriptors, which are invariant under perspective variation, are suitable for hypothesis generation. A means for determining the pose of constructed geometric forms whose algebraic surface descriptions are nonlinear in terms of their orienting parameters is developed. This is done by means of linear functions that are capable of approximating nonlinear forms and determining their parameters. It is shown that biquadratic surfaces are suitable companion-linear forms for cylinder approximation and parameter estimation. The estimates provided the initial parametric approximations necessary for a nonlinear regression stage to fine tune the estimates by fitting the actual nonlinear form to the data. 1993, IEEE. All rights reserved.
