3D shape recognition and retrieval based on multi-modality deep learning

For 3D shape analysis, an effective and efficient feature is the key to popularize its applications in 3D domain where the major challenge lies in designing an effective high-level feature. The three-dimensional shape contains various useful information including visual information, geometric relationships, and other type properties. Thus the strategy of exploring these characteristics is the core of extracting effective 3D shape features. In this paper, we propose a novel 3D feature learning framework which combines different modality data effectively to promote the discriminability of uni-modal feature by using deep learning. The geometric information and visual information are extracted by Convolutional Neural Networks (CNNs) and Convolutional Deep Belief Networks (CDBNs), respectively, and then two independent Deep Belief Networks (DBNs) are employed to learn high-level features from geometric and visual features. Finally, a Restricted Boltzmann Machine (RBM) is trained for mining the deep correlations between different modalities. Extensive experiments demonstrate that the proposed framework achieves better performance.