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

2025-10-07

AI Summary

Autoregressive point cloud generation has long suffered from modeling long-range dependencies due to hand-crafted ordering, hindering global structural fidelity (e.g., symmetry, topological consistency). To address this, we propose PointNSP, a multi-scale autoregressive framework based on next-scale detail prediction. It adopts a coarse-to-fine progressive generation paradigm that aligns autoregression with point-set permutation invariance. Crucially, it introduces hierarchical detail prediction and cross-scale contextual interaction to explicitly enforce global geometric constraints. On ShapeNet, PointNSP achieves state-of-the-art autoregressive performance for dense 8,192-point generation—surpassing leading diffusion models for the first time—while requiring fewer parameters and significantly lower training and inference overhead.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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 emphasizes short-range continuity but undermines the model's capacity to capture long-range dependencies, hindering its ability to enforce global structural properties such as symmetry, consistent topology, and large-scale geometric 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. Experiments on ShapeNet show that PointNSP establishes state-of-the-art (SOTA) generation quality for the first time within the autoregressive paradigm. In addition, it surpasses strong diffusion-based baselines in parameter, training, and inference efficiency. Finally, in dense generation with 8,192 points, PointNSP's advantages become even more pronounced, underscoring its scalability potential.

Problem

• Addresses autoregressive point cloud generation quality gap
• Overcomes sequential bias hindering global structural properties
• Enables coarse-to-fine generation preserving global shape structure

Innovation

• Coarse-to-fine generative framework with multi-scale factorization
• Next-scale prediction paradigm for progressive detail refinement
• Aligns autoregressive objective with permutation-invariant point sets