Recognizing 3D Objects with 3D Information from Stereo Vision

Conventional local feature-based object recognition methods try to recognize learned 3D objects by using unordered local feature matching followed by the verification. However, the matching between unordered feature sets can be ambiguous and, moreover, it is difficult to deal with general shaped 3D objects in the verification stage. In this paper, we present a new framework for general 3D object recognition, which is based on the invariant local features and their 3D information with stereo cameras. We extend the conventional object recognition framework for stereo cameras. Since the proposed method is based on the stereo vision, it is possible to utilize 3D information of local features visible from two cameras.