When reverse engineering a CAD model, it is necessary to integrate information from several views of an object into a common reference frame. Given a rough initial alignment, further pose refinement here uses an improved version of the interactive closes point algorithm. Incremental adjustments are computed simultaneously for all data sets, resulting in a more globally optimal set of transformations. Also, thresholds for removing outlier correspondences are not needed, as the merging data sets are considered as a whole. Motion updates are computed through force-based optimization, using implied springs between data sets. Experiments indicate that even for very rough initial positionings, registration accuracy approaches 25% of the interpoint sampling resolution of the images.

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.

A common need in machine vision is to compute the 3-D rigid transformation that exists between two sets of points for which corresponding pairs have been determined. In this paper a comparative analysis of four popular and efficient algorithms is given. Each computes the translational and rotational components of the transform in closed-form as the solution to a least squares formulation of the problem. They differ in terms of the representation of the transform and the method of solution, using respectively: singular value decomposition of a matrix, orthonormal matrices, unit quaternions and dual quaternions. This comparison presents results of several experiments designed to determine the (1) accuracy in the presence of noise, (2) stability with respect to degenerate data sets, and (3) relative computation time of each approach.

Previous work on the recognition of objects by reasoning about their functionality has not dealt with objects that have moving parts. In this paper we introduce a scenario in which object recognition is accomplished by first deriving an articulated shape model from an observed sequence of 3-D shapes and by then reasoning about the possible functionality of the articulated shape model.

Algorithms for computing the aspect graph representation are generalized to include a larger, more realistic domain of objects known as articulated assemblies those objects composed of rigid parts with articulated connections allowed between parts. The generalization suggests two slightly different representations: one that directly summarizes the possible general views of the object and another (hierarchical) form summarizing the possible general configurations and their respective views. Algorithms are outlined for computing both representations. The generalized aspect graphs of assemblies formed using translational connections are examined.>

An algorithm for computing the aspect graph for a class of curved-surface objects based on an exact parcellation of 3-D viewpoint space is presented. The object class considered is solids of revolution. A detailed analysis of the visual events for this object class is given, as well as an algorithm for constructing the aspect graph. Numerical search techniques, based on a geometric interpretation of the visual events, have been devised to determine those visual event surfaces that cannot be calculated directly. The worst-case complexity of the number of cells in the parcellation of 3-D viewpoint space, and, hence, the number of nodes in the aspect graph, is O(N/sup 4/), where N is the degree of a polynomial that defines the object shape. A summary of the results for 20 different object descriptions is presented, along with a detailed example for a flower vase.>

Currently the aspect graph is computed from the theoretical standpoint of perfect resolution in object shape, the viewpoint and the projected image. This means that the aspect graph may include details that an observer could never see in practice. Introducing the notion of scale into the aspect graph framework provides a mechanism for selecting a level of detail that is "large enough" to merit explicit representation. This effectively allows control over the number of nodes retained in the aspect graph. This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image.>

Currently the aspect graph is computed under the assumption of perfect resolution in the viewpoint, the projected image, and the object shape. Visual detail is represented that an observer might never see in practice. By introducing scale into this framework, a mechanism is provided for selecting levels of detail that are large enough to merit explicit representation, effectively allowing control over the size of the aspect graph. To this end the scale space aspect graph is introduced, and an interpretation of the scale dimension in terms of the spatial extent of image features is considered. A brief example is given for polygons in a plane.>

Several researchers have previously described approaches to 3-D object recognition which use nonlinear optimization to control the matching of features of a 3-D object niodel to features found in an image. Recognition, in this context, includes estimating the parameters of translation and orientation of the object. A major problem acknowledged by previous researchers is how to efficiently choose a set of starting paranleter estimates which will avoid recognition errors due to local minima. The unique contribution of this paper is that it outlines an approach for using the perspective projection aspect gruph representation to alleviate the problems encountered by previous researchers, describes a particular implementation of this general approach, and presents data to illustrate the effectiveness (of the approac!].