Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks

Abstract

Non-rigid 3-D model retrieval is a challenging problem in 3-D shape analysis. Recently, deep learning-based 3-D feature extraction methods have been studied and have achieved better performance than the previous state-of-the-art methods. Inspired by the quadruplet neural networks proposed for learning local image feature descriptors, we propose a novel non-rigid 3-D model retrieval method based on quadruplet convolutional neural networks. For training the proposed networks, the quadruplet samples are first selected using the online sampling method. For each 3-D model, the wave kernel signature descriptor of each vertex is computed, and its corresponding multi-energy shape distribution matrix is constructed as the input of the network. Then, the quadruplet convolutional neural networks are trained using our improved quadruplet loss function, which not only preserves the advantages of existing quadruplet loss functions but also decreases the risk of underfitting. For the query sample, the 3-D shape features are computed using one branch of the trained quadruplet networks. Finally, the retrieval results are obtained by the L2 distance measure. Extensive experimental results have validated the effectiveness of the proposed method.