GaFL: Geometric-aware Feature Learning for universal 3D models recognition

Abstract

Existing methods of 3D model recognition mainly depend on deep learning algorithms. However, the extracted deep feature lacks the geometric information of the models. Thus, these methods fail to identify rigid and non-rigid 3D models simultaneously, i.e., universal 3D models recognition. In this work, we propose a novel method, Geometric-aware Feature Learning (GaFL), to further investigate the combination mechanism of geometric feature and deep learning in universal 3D models recognition. In GaFL, we design the Layer-Projected and Ray-Projected feature extraction policies to obtain depth values, which contain rich geometric information. Furthermore, the sphere convolution is proposed to guarantee the continuity and integrity of the ray feature when feeding into a deep network and the feature inactivation fusion module is designed to achieve the complementarity between layer and ray feature. Finally, the final merged feature vector contains enough geometric information as well as high-level semantic information, which are critical to universal 3D models recognition. In the experiments, GaFL achieves 95.0% and 95.2% classification accuracy in the rigid 3D models dataset ModelNet40 and the non-rigid 3D models dataset SHREC16, respectively, indicating that GaFL is powerful in universal 3D models recognition. Moreover, its significant advantage over state-of-the-art methods has also been validated on three other datasets, i.e., ShapeNet Core55, ScanObjectNN and SHREC15.