Classification of occluded 2D objects using deep learning of 3D shape surfaces

This paper presents a novel deep learning method for partially occluded 2D object classification. A 2D Convolutional Neural Network (CNN) was trained with partial and whole images of the 3D models obtained from different camera views. The efficiency of the proposed method in classifying partial objects in 40 categories is more than 80% in most objects and exceeds 95% in some of them.