Learning Multi View Representation With LSTM for 3 D Shape Recognition and Retrieval
Shape representation for 3 D models is an important topic in computer vision, multimedia analysis, and computer graphics. Recent multiview based methods demonstrate promising performance for 3 D shape recognition and retrieval. However, most multiview based methods ignore the correlations of multiple views or suffer from high computional cost. In this paper, we propose a novel multiview based network architecture for 3 D shape recognition and retrieval. Our network combines convolutional neural networks (CNNs) with long short term memory (LSTM) to exploit the correlative information from multiple views. Well pretrained CNNs with residual connections are first used to extract a low level feature of each view image rendered from a 3 D shape. Then, a LSTM and a sequence voting layer are employed to aggregate these features into a shape descriptor. The highway network and a three step training strategy are also adopted to boost the optimization of the deep network. Experimental results on two public datasets demonstrate that the proposed method achieves promising performance for 3 D shape recognition and the state of the art performance for the 3 D shape retrieval.
IEEE Transactions on Multimedia