A generalized method for 3D object location from single 2D images

-A method is presented to solve for the three-dimensional (3D) object position and orientation from single two-dimensional (2D) images based on a systematic hierarchy of features: primitive features, generalized features, and compound structures. A virtual viewpoint analysis shows that a featurecentered coordinate system allows us to drastically reduce the mathematical complexity of the inverse projection problem for higher order curves. At the heart of the matchings are the matching functions for primitive features, which are: point-to-point correspondence, line-to-line correspondence, and ellipse-to-ellipse correspondence. Parametric matching by generalized features is used to solve for viewpoints at any stage of matching. The generalized features can be viewed as the generalization of features used by previous researchers. The matching method goes "top-down" and then "bottomup", if necessary. Models of the objects are analyzed first. In the top-down process, search starts from the most complex features to the least complex features. In the bottom-up process, the least complex generalized features are used to solve for initial viewpoints. The viewpoints are further refined by matching the compound structures which are obtained by merging the consistent generalized features. The contributions include: (1) solving the problem of ellipse-to-ellipse correspondence, thus extending the class of recognizable objects; (2) the generalized features can assume various configurations, thus enabling the system to detect a multitude of features; (3) parametric matching instead of point-point matching makes the algorithm robust; (4) the top-down-bottom-up strategy combines efficiency with robustness. Computer vision Object recognition Robotics vision Generalized features Parametric matching Object location 3D vision Ellipse-to-ellipse correspondence 2D data