A 3D object recognition and pose estimation system using deep learning method

This paper addresses a 3D object recognition and pose estimation method with a deep learning model. We train two separated Deep Belief Networks (DBN) before connecting the last layers together to train a classifier. By this means, we can simplify the complicated 3D problem to an easier classifier training problem. The deep learning model shows its advantages in learning hierarchical features which greatly facilitate the recognition mission. We apply the new Deep Belief Networks that combine the two traditional DBNs together and assign different poses of objects as different classes in the system. Besides, to overcome the shortcoming in object detection of the deep learning model, a new object detection method based on K-means clustering is presented. We have built a database comprised of 4 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system with two cameras using the new DBNs can achieve high accuracy on 3D object recognition as well as pose estimation.