Viewpoint-invariant image matching and generation of three-dimensional models from two-dimensional imagery

Abstract

A method and system for characterizing features in a source multifeatured three-dimensional object and for locating a best-matching three-dimensional object from a reference database of such objects by performing a viewpoint invariant search among the reference objects. The invention further includes the creation of a three-dimensional representation of the source object by deforming a reference object.

Description

RELATED APPLICATIONS[0001]This application claims priority to and the benefits of U.S. Provisional Applications Ser. Nos. 60 / 452,429, 60 / 452,430 and 60 / 452,431 filed on Mar. 6, 2003 (the entire disclosures of which are hereby incorporated by reference).FIELD OF THE INVENTION[0002]The present invention relates to object modeling and matching systems, and more particularly to the generation of a three-dimensional model of a target object from two- and three-dimensional input.BACKGROUND OF THE INVENTION[0003]In many situations, it is useful to construct a three-dimensional (3D) model of an object when only a partial description of the object is available. In a typical situation, one or more two-dimensional (2D) images of the 3D object may be available, perhaps photographs taken from different viewpoints. A common method of creating a 3D model of a multi-featured object is to start with a base 3D model which describes a generic or typical example of the type of object being modeled, and...

AI Technical Summary

Benefits of technology

This approach enables the generation of highly accurate and efficient 3D models from partial data, improving the verisimilitude of the model and reducing the need for manual intervention, allowing for the creation of 3D models from diverse viewpoints and varying intrinsic object variability.

Problems solved by technology

The main problem with this approach is that the 3D geometry is not highly defined or tuned for the actual target object which is being generated.

However, for any target object with a reasonable range of intrinsic variability, the geometry of the model will still not be well tuned to the target.

This lack of geometric fit will detract from the verisimilitude of the 3D model to the target object.

This usually limits the use of such approaches to situations where the model is being generated in a controlled environment in which the target object can be photographed.

This manual step places a severe limit on the speed with which a 3D model can be generated from 2D imagery.

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