We propose FrePolad: frequency-rectified point latent diffusion, a point
cloud generation pipeline integrating a variational autoencoder (VAE) with a
denoising diffusion probabilistic model (DDPM) for the latent distribution.
FrePolad simultaneously achieves high quality, diversity, and flexibility in
point cloud cardinality for generation tasks while maintaining high
computational efficiency. The improvement in generation quality and diversity
is achieved through (1) a novel frequency rectification module via spherical
harmonics designed to retain high-frequency content while learning the point
cloud distribution; and (2) a latent DDPM to learn the regularized yet complex
latent distribution. In addition, FrePolad supports variable point cloud
cardinality by formulating the sampling of points as conditional distributions
over a latent shape distribution. Finally, the low-dimensional latent space
encoded by the VAE contributes to FrePolad's fast and scalable sampling. Our
quantitative and qualitative results demonstrate the state-of-the-art
performance of FrePolad in terms of quality, diversity, and computational
efficiency.

提出了 FrePolad：一种整合变分自编码器 (VAE) 和去噪扩散概率模型 (DDPM) 用于点云生成的频率校正点潜扩散的流水线，它在保持高效计算的同时实现了生成任务中的高质量、多样性和灵活性，通过 (1) 通过球谐函数设计的新型频率校正模块来保留高频内容，从而学习点云分布；和 (2) 学习正则化但复杂的潜在分布的潜在 DDPM；此外，FrePolad 通过将点的采样归结为潜在形状分布上的条件分布来支持可变的点云基数；最后，由 VAE 编码的低维潜在空间有助于 FrePolad 的快速和可扩展的采样。我们的定量和定性结果证明了 FrePolad 在质量、多样性和计算效率方面的最先进性能。