PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction

Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias reinforces short-range continuity but limits the model’s ability to capture long-range dependencies, thereby weakening its capacity to enforce global structural properties such as symmetry, geometric consistency, and large-scale spatial regularities. Inspired by the level-of-detail (LOD) principle in shape modeling, we propose PointNSP, a coarse-to-fine generative framework that preserves global shape structure at low resolutions and progressively refines fine-grained geometry at higher scales through a next-scale prediction paradigm. This multi-scale factorization aligns the autoregressive objective with the permutation-invariant nature of point sets, enabling rich intra-scale interactions while avoiding brittle fixed orderings. Strictly following the baseline experimental setups, empirical results on ShapeNet benchmark demonstrate that PointNSP achieves state-of-the-art (SOTA) generation quality for the first time within the autoregressive paradigm. Moreover, it surpasses strong diffusion-based baselines in parameter, training, and inference efficiency. Finally, under dense generation with 8,192 points, PointNSP's advantages become even more pronounced, highlighting its strong scalability potential.