Using Photometric Invariants for 3D Object Recognition

Abstract

In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. We derive some photometric properties that are invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We show that recognition does not require a full constancy of colors; rather, it only needs something that remains unchanged under the varying light conditions and poses of the objects. Combining the derived color invariants and spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stability and efficiency of our approach to 3D object recognition.