New Methods for Matching 3D Objects with Single Perspective Views
Résumé
In this paper we analyze the ability of a computer vision system to derive properties of the three-dimensional world from viewing two-dimensional images. We present a new approach which consists of a model-based interpretation of a single perspective image. Image linear features and linear-feature sets are backprojected onto the 3-D space and geometric models are then used for selecting possible solutions. The paper treats two situations: interpretation of scenes resulting from a simple geometric structure (orthogonality) in which case we seek to determine the orientation of this structure relatively to the viewer and recognition of moderately complex structures whose shapes are provided in advance (geometrical and topological properties). The recognition technique is limited to objects containing, among others, straight edges and planar faces. In the first case the computation can be carried out by a parallel algorithm which selects the solution that received the largest number of votes (accumulation space). In the second case an object is uniquely assigned to a set of image features through a search strategy. As a by-product, the position and orientation of each recognized object is determined as well. The method is valid over a wide range of perspective images and it does not require perfect low-level image segmentation. It has been successfully implemented for recognizing a class of industrial parts.
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