Multi-view Moments Embedding Network for 3D Shape Recognition

Benefited from rapid developments of deep learning, 3D shape recognition has become a remarkable subject in computer vision systems. The existing methods of multi-perspective views have shown competitive performance in 3D shape recognition. However, they have not yet fully exploited the information among all views of projection. In this paper, we propose a novel Multi-view Moments Embedding Network(MMEN) for capturing multiple moments information. MMEN obtains the similarity between different views and retains the description of the original view by generating moments matrix for representing the general features of the 3D shape. Additionally, we apply the matrix square-root layer to perform a non-linear scaling to the eigenvalues of the moment embedding matrix. We compare the performance of our proposed network with several state-of-the-art models on the ModelNet datasets, and the results of the average instance/class accuracy demonstrate the promising performance of MMEN on 3D shape recognition.