The method based on view-directional consistency constraints for robust 3D object recognition

This paper proposes a novel geometric verification method to handle 3D viewpoint changes under cluttered scenes for robust object recognition. Since previous voting-based verification approaches, which enable recognition in cluttered scenes, are based on 2D affine transformation, verification accuracy is significantly degraded when viewpoint changes occur for 3D objects that abound in real-world scenes. The method based on view-directional consistency constraints requires that the angle in 3D between observed directions of all matched feature points on two given images must be consistent with the relative pose between the two cameras, whereas the conventional methods consider the consistency of the spatial layout in 2D of feature points in the image. To achieve this, we first embed observed 3D angle parameters into local features when extracting the features. At the verification stage after local feature matching, a voting-based approach identifies the clusters of matches that agree on relative camera pose in advance of full geometric verification. Experimental results demonstrating the superior performance of the proposed method are shown.