In home service robot applications, a home working environment exhibits a different spectral signatures that naturally occurring phenomena. The fact that in-site environment and man made furniture objects display most straight edges bring line features to critical important features in object recognition.
We present an intelligent approach to recognize 3D objects using line structure correspondences. The approach identifies an object and estimates the pose of the object and provides the object recognition information to a home service robot system. This is done by a three-stage perceptual structure based intelligent computations as following.

1. Line structures that human usually used to describe an object is used to represent the object. A set of such representation pairs that share same properties with corresponding model line structures are first generated from images.
2. The structure correspondences are evaluated and ranked by computation of feature coverage that human used to judge recognition certainty. Only the most meaningful correspondences are selected. Each correspondence contributes a pose hypothesis consisting of a similarity transformation.
3. The approximate model pose hypotheses are estimated based on the selected correspondences.

* The framework of the Line-based Recognition