PointSTAR: Continuous Next-Scale Autoregressive Generation for 3D Point Cloud

Autoregressive point cloud generation is complicated by two properties of 3D geometry: point sets have no canonical ordering, and their coordinates are continuous. Existing methods address the first issue through manually ordered point sequences or coarse-to-fine next-scale prediction, but the latter commonly relies on vector-quantized tokens and therefore introduces a discrete bottleneck. We present PointSTAR, which formulates point cloud generation as continuous next-scale autoregressive prediction. PointSTAR first learns a compact hierarchy of progressively decodable continuous latents with a Nested-scale Variational AutoEncoder (NAVE). It then models generation at two complementary levels: inter-scale prediction captures coarse-to-fine structural evolution, while a multi-directional autoregressive flow captures dependencies among latent slots within each scale. This formulation preserves an explicit generation hierarchy without imposing a codebook on continuous geometry. Experiments on ShapeNet report competitive generation quality together with runtime comparisons, while reconstruction, interpolation, and component analyses examine the learned continuous hierarchy. PointSTAR also adapts to point cloud completion and upsampling, suggesting that the hierarchy is useful beyond unconditional generation.