Analyzing the geometric and semantic properties of 3D point cloud data via the deep learning networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. In our study, the authors combine the advantage of voxels and point clouds by presenting a new data form of voxel models, called Layer‐Ring data. This data type can retain the fine description of the 3D data, and keep the high efficiency of feature extraction. After that, based on the Layer‐Ring data, a modern network architecture, called VoxPoint Annular Network (VAN), works on the Layer‐Ring data for the feature extraction and object category prediction. The design idea is based on the edge‐extraction and the coordinate representation for each voxel on the separated layer With the flexible design, our proposed VAN can adapt to the layer's geometric variability and scalability Finally, the extensive experiments and comparisons demonstrate that our approach obtained the notable results with the state‐of‐the‐art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40). Moreover, the tests also proved that 3D shape features could learn efficiently and robustly. All relevant codes will be available at https://github.com/helloFionaQ/Vox‐PointNet....

The Layerizing VoxPoint Annular Convolutional Network for 3D Shape Classification

10.1111/cgf.14145