Three-Dimensional Object Recognition Based On Enhanced Point Pair Features

Aiming at the problems of memory waste and low efficiency in three-dimensional (3D) object recognition algorithm based on original point pair feature (PPF), a 3D object recognition algorithm based on enhanced point pair feature (EPPF) is proposed. By multiplying the fourth component of the original PPF with a sign function, a more distinguishing PPF is obtained, which eliminates the ambiguity of the original PPF. Considering the self-occlusion of the 3D model of the target to be identified, the large numbers of redundant point pairs existing in the target 3D model hash table arc eliminated by means of the viewpoint visibility constraint between the point pairs, which reduces the memory overhead and improves the accuracy and efficiency of the 3D object recognition algorithm. The experimental results on the open dataset and the actual collected dataset show that the proposed 3D object recognition algorithm can improve recognition accuracy and recognition efficiency.