  Using deep learning techniques to process 3D objects has achieved many
successes. However, few methods focus on the representation of 3D objects,
which could be more effective for specific tasks than traditional
representations, such as point clouds, voxels, and multi-view images. In this
paper, we propose a Sphere Node Graph (SN-Graph) to represent 3D objects.
Specifically, we extract a certain number of internal spheres (as nodes) from
the signed distance field (SDF), and then establish connections (as edges)
among the sphere nodes to construct a graph, which is seamlessly suitable for
3D analysis using graph neural network (GNN). Experiments conducted on the
ModelNet40 dataset show that when there are fewer nodes in the graph or the
tested objects are rotated arbitrarily, the classification accuracy of SN-Graph
is significantly higher than the state-of-the-art methods.
