Parameterization-Based Dataset Distillation of 3D Point Clouds through Learnable Shape Morphing

In one paragraph

This method extends parameterization-based dataset distillation to 3D point clouds by storing a small set of coarser-resolution anchor point clouds plus learnable per-sample morphing weights, so that morphing the anchors' shapes generates a more diverse synthetic set than the same memory budget would allow if samples were stored directly; a uniformity-aware matching loss keeps the morphed samples structurally consistent with the originals. The paper reports outperforming existing dataset distillation methods on ModelNet10, ModelNet40, ShapeNet, ScanObjectNN and OmniObject3D.