In this paper we present an algorithm for automatically building models of articulated objects from range data. These models not only describe the surface shape of the object but also describe the kinematics that constrain the movement of one object component in relation to another. This is more difficult than building models of rigid objects because the association of surface measurements to object components must be determined. The algorithm is demonstrated on a difficult object with free-form surfaces. 1 Introduction The ability to automatically acquire geometric models from example objects is useful in a growing number of application areas. In the field of computer graphics, the need for improvements in realism requires more complex models, but manual model construction is time-consuming and difficult. Users of Computer-Aided Design technology would like to be able to make improvements to a manufactured part and then update their CAD model to reflect this. This provides a very eff...

The paper proposes a reliable method for estimating second-order surfaces from 3D range data in the framework of object recognition and localization or object modelling. Instead of estimating such surface individually the approach ts all the surfaces captured in the scene together, taking into account the geometric relationships between them and their speci c characteristics. The technique is compared with other methods through experiments performed on real objects and demonstrates that the use of constrained relationships improves shape estimates.

In this paper we consider one aspect of the problem of automatically constructing geometric models of articulated objects from multiple range images. Automatic model construction has been investigated for rigid objects, but the techniques used do nor extend easily to the articulated case. The problem arises because of the need to register surface measurements taken from different viewpoints into a common reference frame. Registration algorithms generally assume that an object does not change shape from one view to the next, but when automatically building a model of an articulated object, it is necessary for the modes of articulation to be present in the example data. To avoid this problem we propose that raw surface data of articulated objects is first segmented into rigid subsets, corresponding to rigid subcomponents of the object. This allows a model of each subcomponent to be constructed using the conventional approaches and a final, articulated model to be constructed by assembling each of the subcomponent models. We describe an algorithm developed to segment range data into rigid subsets based on surface patch correspondences and present some results for the planar patch case.

The recognition of shapes in images using Pairwise Geometric Histograms has previously been confined to fixed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be extended to the recognition of shapes over a range of scale. In this paper the necessary additions to the existing algorithm are described and the technique is demonstrated on real image data. Hypotheses generated by matching scene shape data to models have previously been resolved using the generalised Hough transform. The robustness of this method can be attributed to its approximation of maximum likelihood statistics. To further improve the robustness of the recognition algorithm and to improve the accuracy to which an objects location, orientation and scale can be determined the generalised Hough transform has been replaced by the probabilistic Hough transform. 1