基于小波分解的自编码器，精准还原分子密度体积的全局与细节结构。
Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes
- 借鉴多尺度小波分解，分层递归量化捕捉不同尺度特征。
- 在ProteinNet3D等数据集上重建质量优于现有3D自编码器。
- 适合结构生物学与体素图像生成研究者使用。
从体素化数据中学习3D形状的鲁棒表征对推动生物医学成像中的AI方法至关重要。然而，当前多数3D计算机视觉方法针对点云、网格或八叉树，而结构生物学与冷冻电镜的原生格式——体素密度图仍相对未被充分探索。我们提出Cryo-SWAN，一种受多尺度小波分解启发的体素基变分自编码器。该模型在感知尺度上执行条件性的粗到细潜在编码与递归残差量化，可准确捕捉分子密度体内的全局几何与高频结构细节。在ModelNet40、BuildingNet及新构建的冷冻电镜体积数据集ProteinNet3D上评估，Cryo-SWAN在重建质量上持续优于当前最优3D自编码器。我们证明了分子密度在学习的潜在空间中按共享几何特征组织，且与扩散模型结合后可实现去噪与条件性形状生成。Cryo-SWAN为数据驱动的结构生物学与体素成像提供了一个实用框架。
原文摘要 · Abstract (English)
Learning robust representations of 3D shapes from voxelized data is essential for advancing AI methods in biomedical imaging. However, most contemporary 3D computer vision approaches operate on point clouds, meshes, or octrees, while volumetric density maps, the native format of structural biology and cryo-EM, remain comparatively underexplored. We present Cryo-SWAN, a voxel-based variational autoencoder inspired by multi-scale wavelet decomposition. The model performs conditional coarse-to-fine latent encoding and recursive residual quantization across perception scales, enabling accurate capture of both global geometry and high-frequency structural detail in molecular density volumes. Evaluated on ModelNet40, BuildingNet, and a newly curated dataset of cryo-EM volumes, ProteinNet3D, Cryo-SWAN consistently improves reconstruction quality over state-of-the-art 3D autoencoders. We demonstrate that the molecular densities organize in learned latent space according to shared geometric features, while integration with diffusion models enables denoising and conditional shape generation. Together, Cryo-SWAN is a practical framework for data-driven structural biology and volumetric imaging.
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