CryoSplat2: Gaussian Splatting for Cryo-EM Ab Initio Reconstruction

Cryogenic electron microscopy (cryo-EM) is a widely used structural biology technique for determining the 3D structures of biomolecules from noisy 2D particle images. In single-particle cryo-EM, *ab initio* reconstruction jointly estimates 3D density maps and unknown particle poses without an initial 3D reference, making the problem severely ill-posed due to the strong coupling between pose estimation and structure reconstruction. Gaussian representations provide an explicit, continuous, and differentiable model of molecular density, with adaptive degrees of freedom and efficient projection, but most existing Gaussian-based cryo-EM methods assume known particle poses. We introduce cryoSplat2, a Gaussian-based framework for particle-level *ab initio* reconstruction that jointly optimizes particle poses and anisotropic Gaussian representations from random initialization. In particular, cryoSplat2 combines frequency marching for coordinated coarse-to-fine pose and structure refinement, adaptive Gaussian densification for progressively increasing representation capacity, and an optimized differentiable splatting operator for efficient pose inference. Experiments on multiple real cryo-EM datasets show that cryoSplat2 robustly reconstructs 3D structures without an initial 3D reference or externally provided particle poses, including challenging cases where cryoSPARC or cryoDRGN-AI fails to converge to the correct structure. The code will be released upon paper publication.