Enforcing 3D Constraints To Improve Object and Scene Recognition

This paper presents an extension to David Lowe’s well-known object recognition algorithm based on his Scale and Feature Invariant Transform (SIFT). One of the benefits of Lowe’s SIFT-based method is that it can recognize objects from only three keypoints. While this capability can be useful in circumstances where the cost of a false negative is high, it’s often the case that false positive are an equal, or greater, concern. We extend Lowe’s algorithm by adding the ability to use 3D constraints during matching. These constraints essentially eliminate false positive matches. We combine our extension with the original algorithm to retain the recognition power of the original method while adding significant robustness against false positives, thereby increasing overall classification power. In addition to improving recognition, our extension returns 3D pose information. Yet, it adds very little computational overhead to Lowe’s original algorithm.