Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion framework and introduces hierarchical levels of latent representations, ranging from point-level to super-point levels, each corresponding to a different spatial resolution. We design a multi-scale latent integration module along with 3D spatial attention to effectively denoise the point-level latent representations conditioned on those from multiple super-point levels. Additionally, we propose a latent consistency model, learned through consistency distillation, that compresses the prior into a one-step generator. This significantly improves sampling efficiency while preserving the performance of the original teacher model. Extensive experiments on standard benchmarks ShapeNet and ShapeNet-Vol demonstrate that MLPCM achieves a 100x speedup in the generation process, while surpassing state-of-the-art diffusion models in terms of both shape quality and diversity.

翻译：一致性模型显著加速了扩散模型的采样过程，在合成高分辨率图像方面取得了令人瞩目的成果。为探索并扩展这些进展至基于点云的三维形状生成领域，我们提出了一种新颖的多尺度潜在点一致性模型。该模型遵循潜在扩散框架，引入了从点级别到超点级别的分层潜在表示，每一层级对应不同的空间分辨率。我们设计了一个多尺度潜在集成模块，结合三维空间注意力机制，以在多个超点级别潜在表示的条件下有效去噪点级别潜在表示。此外，我们提出了一种通过一致性蒸馏学习的潜在一致性模型，将先验压缩为一步生成器。这显著提高了采样效率，同时保持了原始教师模型的性能。在标准基准数据集ShapeNet和ShapeNet-Vol上进行的大量实验表明，MLPCM在生成过程中实现了100倍的加速，同时在形状质量和多样性方面超越了最先进的扩散模型。