Rotation Prediction Based Representative View Locating Framework for 3D Object Recognition. (September 2022) - Jin, Xun; Li, De
Abstract: Finding representative views of 3D objects is a key problem in the field of 3D object analysis. We can obtain most of the crucial information of 3D objects from their representative views. In this paper, we propose a framework for learning the features of multi-view rendered images extracted from 3D objects in order to locate representative views of 3D objects. The learning method includes a reinforcement learning based rotation direction prediction (RDP) method and a deep learning based rotation angle prediction (RAP) method. The RDP uses a deep deterministic policy gradient (DDPG) algorithm to learn rotation policies. We improved DDPG to make RDP more applicable for learning 3D object rotation action. RAP uses a convolutional neural network to predict the rotation angles of representative views. We also propose a 3D object classification network. The network reconstructs the rendered images using an encoder–decoder based rendered image reconstruction method and trains the images composed of the original and reconstructed images. Finally, a series of experiments are conducted to validate the feasibility of the proposed methods. Experimental results show the competitive performance of the proposed framework. Highlights: We propose a classification reliability discrimination and a reward mechanism for learning 3D object rotation policies. We improve the classification performance and demonstrate the optimum angles are oblique angles using RDP and RAP. With the EDIR method, we reconstruct new rendered images without tiny unique details and improve the generalization ability.