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

Recent attempt in dataset distillation has been made to compress large-scale training datasets into compact synthetic versions, significantly reducing memory usage and training costs. While parameterization-based approaches have shown promising results on image datasets, their application to 3D point clouds remains largely unexplored due to the irregular and unordered nature of 3D data. In this paper, we first introduce a parameterization-based dataset distillation framework for 3D point clouds that enables the use of more diverse synthetic samples than conventional methods under the same memory budget. We construct an initial synthetic dataset containing multiple anchor samples with a coarser resolution than the original sample. We also generate new samples by morphing the shapes of the anchor samples with learnable weights to improve the diversity of the synthetic dataset. Moreover, we devise a uniformity-aware matching loss to ensure the structural consistency when comparing the original and synthetic datasets. Extensive experiments conducted on five standard benchmarks—ModelNet10, ModelNet40, ShapeNet, ScanObjectNN, and OmniObject3D—demonstrate that the proposed method effectively optimizes both the synthetic samples and the weights for shape morphing, outperforming existing dataset distillation methods.