Self-Supervised Few-Shot Learning on Point Clouds

Visualization of ball covers The cover-tree approach of using the balls to group the points in a point cloud is visualized in Figure 1. The visualization shows the process of considering balls shown as transparent spheres at different scales with different densities in a cover-tree. Fig 1a represents the top level (root) of cover-tree which covers the point cloud in a single ball i.e., at level i. Fig 1b and Fig 1c shows the balls at lower level with smaller radiuses as the tree is descended. Thus, we learn local features using balls at various levels with different packing densities. A.1 3D Object Classification Training This section provides the implementation details of our proposed self-supervised network.