A novel 3D shape classification algorithm: point-to-vector capsule network

3D shape classification is a basic but challenging task for point clouds analysis. How to learn the discriminative shape descriptors from point clouds is crucial and difficult for this task. This paper proposes a novel point-to-vector capsule (PVC) network, which can obtain effective 3D shape descriptors from point clouds directly. The entire network contains three main steps. Concretely, we firstly build a hierarchical local feature extraction module with geometric information to capture a series of detailed features on point clouds layer by layer. Subsequently, the high-level features are further extracted by a nonlinear feature mapping and then grouped to obtain different and rich feature vectors. These feature vectors are squeezed and packaged into primary capsules to preserve the integrity of the information. Finally, the features are sufficiently integrated and reorganized by the dynamic routing algorithm to form a 3D shape descriptor with high discriminative ability. Compared with the existing methods, the main difference is that the proposed method avoids the use of global pooling and directly constructs the 3D capsule network with geometric structure information into the point clouds shape descriptor learning process. This could effectively promote classification performance. Experimental results on several challenging point clouds datasets demonstrate the superiority and applicability of the proposed method in comparison with state-of-the-art methods in 3D shape classification.