Constructing a compressed latent space through a variational autoencoder (VAE) is the key for efficient 3D diffusion models. This paper introduces COD-VAE, a VAE that encodes 3D shapes into a COmpact set of 1D latent vectors without sacrificing quality. COD-VAE introduces a two-stage autoencoder scheme to improve compression and decoding efficiency. First, our encoder block progressively compresses point clouds into compact latent vectors via intermediate point patches. Second, our triplane-based decoder reconstructs dense triplanes from latent vectors instead of directly decoding neural fields, significantly reducing computational overhead of neural fields decoding. Finally, we propose uncertainty-guided token pruning, which allocates resources adaptively by skipping computations in simpler regions and improves the decoder efficiency. Experimental results demonstrate that COD-VAE achieves 16x compression compared to the baseline while maintaining quality. This enables 20.8x speedup in generation, highlighting that a large number of latent vectors is not a prerequisite for high-quality reconstruction and generation.

本研究解决了3D扩散模型中高效表示3D形状的压缩潜在空间问题。提出的COD-VAE使用两阶段自编码器方案，将3D形状编码为紧凑的1D潜在向量，并通过不损失质量的方式显著提高压缩和解码效率。实验结果表明，COD-VAE相较于基线实现了16倍的压缩，同时生成速度提高了20.8倍，证明了较少的潜在向量也能实现高质量的重建与生成。

用64个潜在向量表示3D形状的3D扩散模型